Machine learning for medical ultrasound: status, methods, and future opportunities
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportun...
Saved in:
| Published in | Abdominal imaging Vol. 43; no. 4; pp. 786 - 799 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.04.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2366-004X 2366-0058 2366-0058 |
| DOI | 10.1007/s00261-018-1517-0 |
Cover
Loading…
| Abstract | Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization. |
|---|---|
| AbstractList | Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization. Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization. |
| Author | Dhyani, Manish Brattain, Laura J. Telfer, Brian A. Grajo, Joseph R. Samir, Anthony E. |
| Author_xml | – sequence: 1 givenname: Laura J. surname: Brattain fullname: Brattain, Laura J. email: brattainl@ll.mit.edu organization: MIT Lincoln Laboratory – sequence: 2 givenname: Brian A. surname: Telfer fullname: Telfer, Brian A. organization: MIT Lincoln Laboratory – sequence: 3 givenname: Manish surname: Dhyani fullname: Dhyani, Manish organization: Department of Internal Medicine, Steward Carney Hospital, Division of Ultrasound, Department of Radiology, Center for Ultrasound Research & Translation, Massachusetts General Hospital – sequence: 4 givenname: Joseph R. surname: Grajo fullname: Grajo, Joseph R. organization: Department of Radiology, Division of Abdominal Imaging, University of Florida College of Medicine – sequence: 5 givenname: Anthony E. surname: Samir fullname: Samir, Anthony E. organization: Division of Ultrasound, Department of Radiology, Center for Ultrasound Research & Translation, Massachusetts General Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29492605$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kV1rFDEUhoNUbK39Ad7IgDdeOHpOMslkvChIqR9QEUTBu5DJZHZTZpM1H4L_vlm3rlrQq5xwnvfkPXkfkiMfvCXkMcILBOhfJgAqsAWULXLsW7hHTigTogXg8uhQd1-PyVlK1wCAgiNS_oAc06EbqAB-Qj590GbtvG0Wq6N3ftXMITYbOzmjl6YsOeoUip9eNSnrXNLz2svrMNVC-6mZSy7RNmG7DTEX77Kz6RG5P-sl2bPb85R8eXP5-eJde_Xx7fuL11et4Uzk1gyjGIBpOVImrZxnIzXXdNBoKeAgBOoeO-SSUT6yYepGai0zE53rrdOGnZLz_dxtGathY301u6htdBsdf6ignfq7491arcJ3xaUUErEOeHY7IIZvxaasNi4Zuyza21CSogADF5L3XUWf3kGvQ4m-rveToowD9pV68qejg5Vf310B3AMmhpSinQ8Igtqlqvapqpqq2qWqoGr6OxrjahQu7JZyy3-VdK9M9RW_svG36X-LbgB0CrYN |
| CitedBy_id | crossref_primary_10_3390_s23042226 crossref_primary_10_1016_j_ultrasmedbio_2023_03_024 crossref_primary_10_1109_TIM_2023_3243675 crossref_primary_10_1109_TMI_2020_3021560 crossref_primary_10_3390_diagnostics14161761 crossref_primary_10_1038_s41746_019_0094_0 crossref_primary_10_3390_jimaging9100225 crossref_primary_10_1016_j_femme_2019_09_001 crossref_primary_10_1007_s13721_024_00491_0 crossref_primary_10_1002_mp_15856 crossref_primary_10_3390_s24051664 crossref_primary_10_13181_mji_oa_236765 crossref_primary_10_1016_j_clinimag_2022_09_010 crossref_primary_10_1038_s41598_023_42000_9 crossref_primary_10_3389_fmed_2022_945698 crossref_primary_10_3390_diagnostics11101859 crossref_primary_10_1016_j_jacr_2019_06_004 crossref_primary_10_1371_journal_pone_0246611 crossref_primary_10_1002_ird3_114 crossref_primary_10_1080_17434440_2023_2243208 crossref_primary_10_3389_fimmu_2025_1460673 crossref_primary_10_1016_j_clinph_2019_01_024 crossref_primary_10_1002_uog_22087 crossref_primary_10_1016_j_medengphy_2022_103877 crossref_primary_10_1016_j_jconrel_2023_08_035 crossref_primary_10_1007_s11548_024_03309_6 crossref_primary_10_1016_j_csbj_2020_08_003 crossref_primary_10_3390_cancers13040738 crossref_primary_10_1155_2022_7451185 crossref_primary_10_1016_j_adengl_2021_11_007 crossref_primary_10_1016_j_media_2020_101793 crossref_primary_10_1080_1448837X_2024_2429920 crossref_primary_10_3390_app14052152 crossref_primary_10_1016_j_iswa_2023_200183 crossref_primary_10_1109_TUFFC_2020_3002249 crossref_primary_10_3390_diagnostics13040740 crossref_primary_10_1016_j_compmedimag_2023_102290 crossref_primary_10_2174_1574887115666200621183459 crossref_primary_10_1002_mp_16339 crossref_primary_10_1002_jum_16194 crossref_primary_10_1016_j_inffus_2024_102690 crossref_primary_10_1016_j_ultrasmedbio_2022_06_011 crossref_primary_10_2139_ssrn_4157385 crossref_primary_10_1007_s10278_022_00625_8 crossref_primary_10_1016_j_ultrasmedbio_2020_05_016 crossref_primary_10_3390_ai3030043 crossref_primary_10_1016_j_addr_2021_114043 crossref_primary_10_3390_diagnostics13243665 crossref_primary_10_1016_j_imavis_2024_105338 crossref_primary_10_3389_fonc_2024_1438923 crossref_primary_10_1016_j_iliver_2022_11_001 crossref_primary_10_1016_j_ymeth_2022_03_014 crossref_primary_10_1038_s41598_023_37319_2 crossref_primary_10_1155_2022_2645607 crossref_primary_10_1039_D4RA04987J crossref_primary_10_1109_ACCESS_2020_2977962 crossref_primary_10_1016_j_theriogenology_2024_12_021 crossref_primary_10_1016_j_ultrasmedbio_2023_01_006 crossref_primary_10_1007_s11548_018_1788_5 crossref_primary_10_1016_j_artmed_2023_102541 crossref_primary_10_1001_jamanetworkopen_2022_17854 crossref_primary_10_1002_admt_202301732 crossref_primary_10_3390_cancers13040790 crossref_primary_10_1002_acm2_13724 crossref_primary_10_1002_mp_16568 crossref_primary_10_1021_acsanm_3c01442 crossref_primary_10_1016_j_media_2020_101819 crossref_primary_10_1515_bmt_2022_0489 crossref_primary_10_1002_jum_15819 crossref_primary_10_1080_03772063_2025_2462790 crossref_primary_10_1097_RUQ_0000000000000683 crossref_primary_10_1155_2022_8482022 crossref_primary_10_1016_j_compbiomed_2023_107872 crossref_primary_10_3389_fmed_2021_777142 crossref_primary_10_3389_fonc_2023_1154200 crossref_primary_10_3390_s22010245 crossref_primary_10_5005_jp_journals_10009_1702 crossref_primary_10_1002_jum_15403 crossref_primary_10_3390_diagnostics9020061 crossref_primary_10_1097_TA_0000000000004425 crossref_primary_10_3390_diagnostics11020292 crossref_primary_10_1016_j_survophthal_2018_09_002 crossref_primary_10_1002_mp_13521 crossref_primary_10_3390_app13148427 crossref_primary_10_3390_bios11120522 crossref_primary_10_1002_jmri_29294 crossref_primary_10_1038_s41598_022_21724_0 crossref_primary_10_1038_s41746_023_00774_2 crossref_primary_10_1080_07357907_2024_2403088 crossref_primary_10_1038_s41587_023_01800_0 crossref_primary_10_3389_fonc_2023_1197447 crossref_primary_10_3390_biomedicines13040784 crossref_primary_10_1109_TUFFC_2023_3264832 crossref_primary_10_3390_app11031127 crossref_primary_10_1002_jum_16045 crossref_primary_10_1002_uog_23530 crossref_primary_10_3390_app11041690 crossref_primary_10_32604_iasc_2023_023474 crossref_primary_10_1177_01617346231220000 crossref_primary_10_3389_fonc_2024_1440881 crossref_primary_10_3389_fendo_2023_1137700 crossref_primary_10_3390_s24237568 crossref_primary_10_33590_emjreprohealth_20_00100 crossref_primary_10_3390_make2030010 crossref_primary_10_1007_s00330_021_07850_9 crossref_primary_10_1016_j_cmpb_2021_106382 crossref_primary_10_4103_jmu_jmu_182_21 crossref_primary_10_33151_ajp_19_948 crossref_primary_10_1080_16878507_2021_1984150 crossref_primary_10_1155_2022_9042954 crossref_primary_10_1155_2022_5918686 crossref_primary_10_1080_21681163_2023_2179343 crossref_primary_10_1111_exsy_12789 crossref_primary_10_1142_S2196888824500210 crossref_primary_10_1007_s10278_020_00356_8 crossref_primary_10_1007_s00772_020_00670_z crossref_primary_10_1088_1361_6560_abae06 crossref_primary_10_1007_s40620_023_01878_4 crossref_primary_10_1145_3447243 crossref_primary_10_2174_1573405620666230529112655 crossref_primary_10_1007_s10439_024_03615_9 crossref_primary_10_3390_diagnostics13030417 crossref_primary_10_1007_s40883_022_00273_y crossref_primary_10_1016_j_zemedi_2018_11_002 crossref_primary_10_1016_j_ad_2021_07_003 crossref_primary_10_1117_1_JMI_11_4_044505 crossref_primary_10_1177_1742271X211072473 crossref_primary_10_3390_diagnostics14111158 crossref_primary_10_2174_1573405619666230306101012 crossref_primary_10_1007_s00330_020_07181_1 crossref_primary_10_1038_s41597_025_04464_4 crossref_primary_10_1109_TBCAS_2021_3120886 crossref_primary_10_3390_biomedinformatics4010002 crossref_primary_10_1002_jum_15868 crossref_primary_10_1109_TETCI_2024_3377676 crossref_primary_10_3233_CH_221521 crossref_primary_10_1136_archdischild_2018_316263 crossref_primary_10_1007_s42600_020_00118_z crossref_primary_10_1109_TVCG_2021_3114797 crossref_primary_10_1186_s43166_024_00238_9 crossref_primary_10_1021_acssensors_4c00124 crossref_primary_10_1109_OJEMB_2024_3414974 crossref_primary_10_1038_s44222_024_00245_7 crossref_primary_10_3390_cancers16091700 crossref_primary_10_3389_fped_2021_634092 crossref_primary_10_1007_s00404_024_07479_1 crossref_primary_10_1016_j_ad_2021_07_014 crossref_primary_10_3390_metabo14010011 crossref_primary_10_1371_journal_pone_0263106 |
| Cites_doi | 10.1016/j.ultrasmedbio.2016.03.022 10.1016/S0893-6080(05)80131-5 10.2214/AJR.14.14203 10.1371/journal.pone.0108337 10.1587/transinf.2013EDP7464 10.7150/thno.18650 10.1016/j.media.2014.12.006 10.1148/radiol.14140434 10.1016/0169-7439(87)80084-9 10.1016/j.compbiomed.2013.10.029 10.1109/36.752194 10.1111/rssa.12227 10.1118/1.3267037 10.1016/j.ultrasmedbio.2011.10.022 10.1016/j.media.2013.04.001 10.1007/978-3-319-44100-9_8 10.1146/annurev-bioeng-071812-152416 10.1109/TMI.2006.877092 10.1038/s41598-017-02187-0 10.1155/2016/7987212 10.1016/0893-6080(89)90020-8 10.1016/j.media.2012.02.005 10.2214/AJR.15.14676 10.1016/j.media.2013.10.002 10.1002/jcu.22382 10.1148/radiol.14140839 10.1016/j.neucom.2016.01.074 10.1089/thy.2016.0372 10.1016/j.ejrad.2011.01.110 10.1016/j.cviu.2017.04.002 10.1007/s10278-014-9757-1 10.1162/neco.2009.10-08-881 10.1016/j.ultrasmedbio.2015.11.016 10.1007/978-1-4899-4467-2 10.1007/978-3-319-43775-0_26 10.1148/rg.2017160116 10.1016/S0893-6080(03)00115-1 10.1016/j.ultrasmedbio.2016.01.013 10.1016/j.jhep.2012.12.021 10.1016/j.ultrasmedbio.2015.01.001 10.1109/72.554195 10.1016/0893-6080(90)90004-5 10.1007/BF01000274 10.1007/978-3-319-60964-5_6 10.1016/j.ultras.2016.08.004 10.1016/j.ultrasmedbio.2015.11.014 10.1109/JBHI.2016.2631401 10.1214/09-SS054 10.1109/JBHI.2016.2636665 10.1016/j.ultrasmedbio.2013.09.032 10.1007/s10278-016-9929-2 10.1016/j.ejrad.2010.12.013 10.1038/nature14539 10.1007/978-0-387-31439-6_308 10.1177/016173469301500401 10.1055/s-2008-1027806 10.1146/annurev-bioeng-071516-044442 10.1016/j.ejrad.2014.01.011 10.1016/j.ultrasmedbio.2014.09.003 10.1016/j.clinbiomech.2014.11.011 10.1017/S026988899700101X 10.1007/s11548-014-1133-6 10.1016/j.jhep.2014.04.044 10.7863/ultra.15.11017 10.1016/j.ultrasmedbio.2015.10.024 10.1016/j.ejrad.2014.11.019 10.3748/wjg.v20.i13.3590 10.1007/s00330-016-4534-9 10.1007/s00330-016-4427-y 10.1212/WNL.0b013e3182604395 10.1023/A:1018628609742 10.1016/j.neunet.2014.09.003 10.1016/j.ultrasmedbio.2015.05.015 10.1016/j.ijleo.2014.01.114 10.1109/TIP.2011.2169273 10.1007/s10462-007-9052-3 10.1016/j.ultrasmedbio.2017.05.002 10.1007/s10278-014-9754-4 10.1016/j.media.2016.10.007 10.4103/0973-1482.147382 10.1016/S0169-023X(97)00053-0 10.1148/radiology.143.1.7063747 10.1118/1.4862508 10.1243/09544119JEIM604 10.2214/AJR.17.18056 10.1016/j.inffus.2016.11.007 10.7863/ultra.33.2.197 10.1016/j.ultrasmedbio.2016.10.004 10.4111/kju.2014.55.9.587 10.1016/j.neucom.2014.09.066 10.1109/TUFFC.2012.2377 10.1016/j.phpro.2015.08.263 10.7863/ultra.32.12.2185 10.1016/j.ultrasmedbio.2014.11.010 10.1016/j.eswa.2016.09.006 10.1007/s00330-017-5192-2 10.1109/CVPR.2015.7298594 10.1016/j.media.2017.07.005 10.1109/ISBI.2017.7950609 10.1109/BIBM.2016.7822557 10.1109/ISBI.2017.7950519 10.1155/2014/708279 10.1002/mp.12453 10.1109/SPMB.2016.7846873 10.1007/978-3-319-46723-8_24 10.1007/978-3-319-24553-9_62 10.1117/12.912188 10.1117/12.2254581 10.1109/CVPR.2015.7298965 10.1109/ICRCICN.2015.7434224 10.1109/ISBI.2016.7493384 10.1145/2671188.2749408 10.1117/12.2216396 10.1007/978-3-319-24574-4_82 10.1109/CVPR.2013.465 10.1007/978-3-319-60964-5_9 |
| ContentType | Journal Article |
| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 Abdominal Radiology is a copyright of Springer, (2018). All Rights Reserved. |
| Copyright_xml | – notice: Springer Science+Business Media, LLC, part of Springer Nature 2018 – notice: Abdominal Radiology is a copyright of Springer, (2018). All Rights Reserved. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7RV 7X7 7XB 88E 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. KB0 LK8 M0S M1P M7P M7Z NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM |
| DOI | 10.1007/s00261-018-1517-0 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) ProQuest Nursing and Allied Health Journals - PSU access expires 11/30/25. Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials Biological Science Database ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biological Science Database Biochemistry Abstracts 1 Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Biochemistry Abstracts 1 ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Computer Science Database MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2366-0058 |
| EndPage | 799 |
| ExternalDocumentID | PMC5886811 29492605 10_1007_s00261_018_1517_0 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Review |
| GrantInformation_xml | – fundername: National Institute of Biomedical Imaging and Bioengineering grantid: HHSN268201300071 C; K23 EB020710 funderid: http://dx.doi.org/10.13039/100000070 – fundername: NIBIB NIH HHS grantid: K23 EB020710 – fundername: NIBIB NIH HHS grantid: HHSN268201300071 C |
| GroupedDBID | -EM 04C 0R~ 29~ 2J2 2JN 2JY 2KG 2KM 2LR 406 53G 5VS 7RV 7X7 88E 8AO 8FH 8FI 8FJ 95- AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAYIU AAYTO AAYZH ABAKF ABDZT ABECU ABFTV ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABLJU ABMQK ABNWP ABPLI ABQBU ABSXP ABTEG ABTKH ABTMW ABUWG ABWNU ABXPI ACAOD ACDTI ACGFS ACHSB ACHVE ACIWK ACKNC ACMDZ ACMLO ACOKC ACPIV ACPRK ACUDM ACZOJ ADBBV ADINQ ADJJI ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGNC AEJHL AEJRE AEKMD AEMSY AEOHA AESKC AEVLU AEXYK AFBBN AFEXP AFJLC AFKRA AFLOW AFQWF AFRAH AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGVAE AGWZB AGYKE AHBYD AHIZS AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AILAN AITGF AJBLW AJOOF AJRNO AJZVZ ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AOCGG ARAPS ARMRJ AXYYD AZFZN B-. BBNVY BDATZ BENPR BGLVJ BGNMA BHPHI BMSDO BSONS CCPQU CSCUP DDRTE DNIVK DPUIP EBLON EBS EIHBH EIOEI EJD EMB EMOBN FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA GGCAI GNWQR GRRUI H13 HCIFZ HMCUK IKXTQ IMOTQ ITM IWAJR J-C JZLTJ K7- KOV LK8 LLZTM M1P M4Y M7P NAPCQ NPVJJ NQJWS NU0 O9J PQQKQ PROAC PSQYO PT4 QOR R89 ROL RSV S16 SCLPG SDE SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZ9 SZN TSG UG4 UKHRP UOJIU UTJUX UZXMN VFIZW W23 YLTOR Z7X Z83 Z87 Z8V Z8W Z91 ZMTXR AAPKM AAYXX ABBRH ABDBE ABFSG ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT CGR CUY CVF ECM EIF NPM --- .86 .VR 06C 06D 0VY 1N0 23M 2~H 30V 36B 3V. 4.4 408 409 40D 40E 5GY 5RE 67Z 6NX 7XB 8FD 8FE 8FG 8FK 8TC 95. 95~ 96X AAWCG AAWTL AAYQN ABBXA ABOCM ACOMO ADHIR ADIMF AEGAL AENEX AEPYU AETLH AFWTZ AHAVH AIIXL AKMHD ALWAN AZQEC BA0 BKEYQ BPHCQ BVXVI CS3 DL5 DWQXO ESBYG EX3 F5P FEDTE FR3 G-Y G-Z GGRSB GJIRD GNUQQ GQ7 GQ8 GXS HF~ HG5 HMJXF HQYDN HRMNR HVGLF I09 IHE IJ- IXC IZIGR IZQ I~Z J0Z JQ2 K6V K9. KDC KPH LAS M7Z MA- N9A NB0 O93 O9I OAM OVD P19 P62 P64 P9S PF0 PJZUB PKEHL PPXIY PQEST PQGLB PQUKI PRINS Q2X QOK QOS R9I RHV RPX RRX S27 S37 S3B SAP SDH SHX SMD T13 TSV TT1 TUC U2A U9L VC2 WH7 WJK WK8 WOW Z45 ZOVNA 7X8 ABRTQ 5PM |
| ID | FETCH-LOGICAL-c536t-c9b6903a8b238e8ffc8a5a29a1e2019661a714158325b39d4b2ee3cd2fb394ac3 |
| IEDL.DBID | AGYKE |
| ISSN | 2366-004X 2366-0058 |
| IngestDate | Thu Aug 21 14:14:32 EDT 2025 Tue Aug 05 11:38:50 EDT 2025 Fri Jul 25 19:39:58 EDT 2025 Thu Apr 03 07:06:12 EDT 2025 Thu Apr 24 23:10:20 EDT 2025 Tue Jul 01 02:12:28 EDT 2025 Fri Feb 21 02:43:45 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Deep learning Sonography Medical ultrasound Elastography Machine learning |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c536t-c9b6903a8b238e8ffc8a5a29a1e2019661a714158325b39d4b2ee3cd2fb394ac3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 Suggested reviewers Nando de Freitas (nandodefreitas@google.com) Note – the authors know some of the reviewers, but have not approached any of them regarding this review Julien Cornebise (julien@cornebise.com) Brian Garra (bgarra@gmail.com) Alison Noble (alison.noble@eng.ox.ac.uk) Johnathan Scalera (Jonathan.Scalera@bmc.org) Mark Palmeri (mark.palmeri@duke.edu) |
| OpenAccessLink | https://link.springer.com/content/pdf/10.1007%2Fs00261-018-1517-0.pdf |
| PMID | 29492605 |
| PQID | 2009235017 |
| PQPubID | 31175 |
| PageCount | 14 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_5886811 proquest_miscellaneous_2009568574 proquest_journals_2009235017 pubmed_primary_29492605 crossref_primary_10_1007_s00261_018_1517_0 crossref_citationtrail_10_1007_s00261_018_1517_0 springer_journals_10_1007_s00261_018_1517_0 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2018-04-01 |
| PublicationDateYYYYMMDD | 2018-04-01 |
| PublicationDate_xml | – month: 04 year: 2018 text: 2018-04-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: United States |
| PublicationTitle | Abdominal imaging |
| PublicationTitleAbbrev | Abdom Radiol |
| PublicationTitleAlternate | Abdom Radiol (NY) |
| PublicationYear | 2018 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Oh, Lee, Seo (CR28) 2014; 55 Antropova, Huynh, Giger (CR74) 2017 De Mantaras, Armengol (CR34) 1998; 25 Nascimento, Silva, da Silva, Pereira, Costa, Costa Filho (CR118) 2016; 32 Shi, Zhou, Liu, Zhang, Lu, Wang (CR114) 2016; 194 Sang, Wang, Xu, Cai (CR22) 2017; 7 Namburete, Stebbing, Kemp, Yaqub, Papageorghiou, Noble (CR77) 2015; 21 Zhang, Ng, Lee, Fu (CR20) 2014; 9 Singh, Verma, Thoke (CR115) 2016; 66 Yuan, Quan, Yunxiao, Jian, Zhu (CR29) 2015; 11 Menchón-Lara, Sancho-Gómez (CR89) 2015; 151 Taljanovic (CR18) 2017; 37 Yu, Tan, Sng, Li, Sia (CR129) 2015; 41 Rouvière (CR21) 2017; 27 Wold, Esbensen, Geladi (CR41) 1987; 2 Poynard (CR7) 2013; 58 Hwang, Lee, Kim, Jiang, Kim (CR121) 2015; 26 Torresani (CR37) 2014 Aramendía-Vidaurreta, Cabeza, Villanueva, Navallas, Alcázar (CR131) 2016; 42 Milletari (CR92) 2017; 164 Mohri, Rostamizadeh, Talwalkar (CR38) 2012 Woo, Suh, Kim, Cho, Kim (CR23) 2017; 209 Zhang (CR111) 2016; 72 Soh, Tsatsoulis (CR39) 1999; 37 Khamis, Zurakhov, Azar, Raz, Friedman, Adam (CR102) 2017; 36 CR40 Arlot, Celisse (CR61) 2010; 4 Wang, Guo, Shi, Pan, Bai, Ai (CR106) 2012; 81 Kohavi (CR60) 1995; 14 Sultan, Xiong, Zafar, Schultz, Langer, Sehgal (CR84) 2015; 41 Mitchell (CR33) 1997 Wang, Summers (CR1) 2012; 16 Chauhan, Sultan, Furth, Jones, Khungar, Sehgal (CR85) 2016; 44 Lawrence, Giles, Tsoi, Back (CR55) 1997; 8 Ghose (CR83) 2013; 17 Gatos (CR109) 2017; 43 Baur (CR31) 2017 Samir (CR8) 2014; 274 CR59 Maclin, Dempsey (CR64) 1992; 16 CR57 Shen, Wu, Suk (CR2) 2017; 19 CR54 CR53 Giger, Karssemeijer, Schnabel (CR65) 2013; 15 CR52 CR51 CR50 Chen, Wu, Liao, Zheng, Liao, Jannin, Cattin, Lee (CR93) 2016 Turaga (CR58) 2010; 22 Shan, Alam, Garra, Zhang, Ahmed (CR66) 2016; 42 Kotsiantis, Zaharakis, Pintelas (CR36) 2006; 26 Bishop (CR32) 2006 LeCun, Bengio, Hinton (CR49) 2015; 521 Kim, Jang (CR25) 2014; 20 Noble (CR80) 2010; 224 Hanley, McNeil (CR62) 1982; 143 CR69 Takagi, Kondo, Nakamura, Takiguchi (CR133) 2014; E97D Park, Son, Han, Youk, Kim, Park (CR15) 2015; 84 Xiao (CR107) 2014; 40 Wu, Chen, Ding (CR112) 2014; 125 Dutton, Conroy (CR35) 1997; 12 Noe (CR86) 2017; 34 Suykens, Vandewalle (CR44) 1999; 9 Torbati, Ayatollahi, Kermani (CR81) 2014; 44 Wang, Li, Wan, Li, Tang (CR11) 2017; 43 Leshno, Lin, Pinkus, Schocken (CR47) 1993; 6 Yang, Rossi, Jani, Mao, Curran, Liu (CR82) 2016 Eby (CR16) 2015; 30 Sjogren, Leo, Feldman, Gwin (CR135) 2016; 35 Cheng, Malhi (CR73) 2017; 30 Kalyan, Jakhia, Lele, Joshi, Chowdhary (CR123) 2014 Wu, Lin, Moon (CR116) 2015; 28 Pass, Jafari, Rowbotham, Hensor, Gupta, Robinson (CR17) 2017; 27 Sigrist, Liau, El Kaffas, Chammas, Willmann (CR103) 2017; 7 CR75 Srivastava, Darras, Wu, Rutkove (CR127) 2012; 79 Lekadir (CR72) 2017; 21 Westwood (CR27) 2013; 17 Dhyani, Li, Samir, Stephen (CR13) 2017 Zeng, Ustun, Rudin (CR113) 2017; 180 Subramanya, Kumar, Mukherjee, Saini (CR132) 2015; 28 Anvari, Dhyani, Stephen, Samir (CR12) 2016; 206 Konig, Steffen, Rak, Neumann, von Rohden, Tonnies (CR126) 2015; 10 Shan, Cheng, Wang (CR117) 2012; 38 CR3 Sheet (CR128) 2014; 18 Liu (CR9) 2016; 42 CR130 Hornik, Stinchcombe, White (CR46) 1989; 2 Brattain, Telfer, Liteplo, Noble (CR124) 2013; 32 CR87 Pellot-Barakat, Lefort, Chami, Labit, Frouin, Lucidarme (CR105) 2015; 41 Garra, Krasner, Horii, Ascher, Mun, Zeman (CR63) 1993; 15 Cunningham, Harding, Loram, Hernández, González-Castro, González-Castro (CR76) 2017 Schmidhuber (CR67) 2015; 61 White (CR45) 1990; 3 Cary, Reamer, Sultan, Mohler, Sehgal (CR78) 2014; 41 Choi (CR70) 2017; 27 Strobel (CR26) 2008; 29 Cortes, Vapnik (CR43) 1995; 20 Hiramatsu, Muramatsu, Kobayashi, Hara, Fujita (CR71) 2017 Aubry, Nueffer, Tanter, Becce, Vidal, Michel (CR19) 2014; 274 Veeramani, Muthusamy (CR125) 2016; 29 Wang (CR10) 2017; 43 CR99 CR98 Liaw, Wiener (CR42) 2002; 2 CR97 Rouze, Wang, Palmeri, Nightingale (CR104) 2012; 59 CR96 CR95 CR94 Caxinha (CR134) 2015; 70 CR91 CR90 Ferraioli, Parekh, Levitov, Filice (CR6) 2014; 33 Noble, Boukerroui (CR79) 2006; 25 Ding, Cheng, Huang, Zhang, Liu (CR14) 2012; 81 Geisser (CR48) 1993 Jamieson, Giger, Drukker, Li, Yuan, Bhooshan (CR120) 2009; 37 Cassinotto (CR5) 2014; 61 Ravi (CR4) 2017; 21 Suganya, Kirubakaran, Rajaram (CR122) 2014 Bhatia, Lam, Pang, Wang, Ahuja (CR108) 2016; 42 Marcomini, Carneiro, Schiabel (CR119) 2016; 2016 Carneiro, Nascimento, Freitas (CR88) 2012; 21 Gao, Li, Loomes, Wang (CR100) 2017; 36 CR101 Zhang, Xiao, Chen, Wang, Zheng (CR110) 2015; 41 Li (CR30) 2014; 83 Matsugu, Mori, Mitari, Kaneda (CR56) 2003; 16 Carbonell, Michalski, Mitchell (CR68) 1983 D’Onofrio, Crosara, De Robertis, Canestrini, Mucelli (CR24) 2015; 205 TW Cary (1517_CR78) 2014; 41 MH Noe (1517_CR86) 2017; 34 ADJ Baur (1517_CR31) 2017 K Lekadir (1517_CR72) 2017; 21 Y Xiao (1517_CR107) 2014; 40 M D’Onofrio (1517_CR24) 2015; 205 F Milletari (1517_CR92) 2017; 164 M Matsugu (1517_CR56) 2003; 16 T Poynard (1517_CR7) 2013; 58 R Kohavi (1517_CR60) 1995; 14 S Woo (1517_CR23) 2017; 209 Y LeCun (1517_CR49) 2015; 521 S Ghose (1517_CR83) 2013; 17 JA Noble (1517_CR80) 2010; 224 Y Hiramatsu (1517_CR71) 2017 SK Veeramani (1517_CR125) 2016; 29 A Chauhan (1517_CR85) 2016; 44 Q Zhang (1517_CR110) 2015; 41 J Shan (1517_CR117) 2012; 38 B Liu (1517_CR9) 2016; 42 T Srivastava (1517_CR127) 2012; 79 AI Namburete (1517_CR77) 2015; 21 S Arlot (1517_CR61) 2010; 4 SB Kotsiantis (1517_CR36) 2006; 26 M Mohri (1517_CR38) 2012 C Pellot-Barakat (1517_CR105) 2015; 41 N Antropova (1517_CR74) 2017 H Khamis (1517_CR102) 2017; 36 SF Eby (1517_CR16) 2015; 30 G Carneiro (1517_CR88) 2012; 21 K Wu (1517_CR112) 2014; 125 CM Bishop (1517_CR32) 2006 JA Noble (1517_CR79) 2006; 25 ZL Wang (1517_CR11) 2017; 43 YN Hwang (1517_CR121) 2015; 26 S Geisser (1517_CR48) 1993 RM Menchón-Lara (1517_CR89) 2015; 151 M Caxinha (1517_CR134) 2015; 70 M Wang (1517_CR10) 2017; 43 S Aubry (1517_CR19) 2014; 274 S Yu (1517_CR129) 2015; 41 K Hornik (1517_CR46) 1989; 2 K Takagi (1517_CR133) 2014; E97D J Shan (1517_CR66) 2016; 42 PS Maclin (1517_CR64) 1992; 16 ZJ Zhang (1517_CR20) 2014; 9 TM Mitchell (1517_CR33) 1997 N Torbati (1517_CR81) 2014; 44 L Sang (1517_CR22) 2017; 7 D Shen (1517_CR2) 2017; 19 TK Kim (1517_CR25) 2014; 20 1517_CR101 J Schmidhuber (1517_CR67) 2015; 61 A Anvari (1517_CR12) 2016; 206 LR Sultan (1517_CR84) 2015; 41 1517_CR99 TH Oh (1517_CR28) 2014; 55 1517_CR96 RM Sigrist (1517_CR103) 2017; 7 KD Marcomini (1517_CR119) 2016; 2016 1517_CR95 1517_CR98 1517_CR97 1517_CR91 1517_CR94 1517_CR90 S Wang (1517_CR1) 2012; 16 H White (1517_CR45) 1990; 3 J Zeng (1517_CR113) 2017; 180 JA Suykens (1517_CR44) 1999; 9 D Strobel (1517_CR26) 2008; 29 R Suganya (1517_CR122) 2014 NC Rouze (1517_CR104) 2012; 59 C Cortes (1517_CR43) 1995; 20 O Rouvière (1517_CR21) 2017; 27 1517_CR87 X Yang (1517_CR82) 2016 S Wold (1517_CR41) 1987; 2 M Leshno (1517_CR47) 1993; 6 KSS Bhatia (1517_CR108) 2016; 42 J Ding (1517_CR14) 2012; 81 ML Giger (1517_CR65) 2013; 15 C Cassinotto (1517_CR5) 2014; 61 D Sheet (1517_CR128) 2014; 18 D Ravi (1517_CR4) 2017; 21 B Pass (1517_CR17) 2017; 27 AR Jamieson (1517_CR120) 2009; 37 LJ Brattain (1517_CR124) 2013; 32 1517_CR75 X Gao (1517_CR100) 2017; 36 CDL Nascimento (1517_CR118) 2016; 32 Q Zhang (1517_CR111) 2016; 72 JG Carbonell (1517_CR68) 1983 K Kalyan (1517_CR123) 2014 PM Cheng (1517_CR73) 2017; 30 I Gatos (1517_CR109) 2017; 43 G Ferraioli (1517_CR6) 2014; 33 Z Yuan (1517_CR29) 2015; 11 1517_CR69 1517_CR3 L Torresani (1517_CR37) 2014 AE Samir (1517_CR8) 2014; 274 M Westwood (1517_CR27) 2013; 17 L-K Soh (1517_CR39) 1999; 37 BS Garra (1517_CR63) 1993; 15 AR Sjogren (1517_CR135) 2016; 35 W Li (1517_CR30) 2014; 83 SC Turaga (1517_CR58) 2010; 22 V Aramendía-Vidaurreta (1517_CR131) 2016; 42 1517_CR59 RL De Mantaras (1517_CR34) 1998; 25 1517_CR57 1517_CR52 S Lawrence (1517_CR55) 1997; 8 1517_CR51 1517_CR54 MS Taljanovic (1517_CR18) 2017; 37 1517_CR53 WJ Wu (1517_CR116) 2015; 28 A Liaw (1517_CR42) 2002; 2 J Wang (1517_CR106) 2012; 81 M Dhyani (1517_CR13) 2017 1517_CR50 BK Singh (1517_CR115) 2016; 66 R Cunningham (1517_CR76) 2017 AY Park (1517_CR15) 2015; 84 YJ Choi (1517_CR70) 2017; 27 F Chen (1517_CR93) 2016 MB Subramanya (1517_CR132) 2015; 28 J Shi (1517_CR114) 2016; 194 1517_CR130 1517_CR40 JA Hanley (1517_CR62) 1982; 143 T Konig (1517_CR126) 2015; 10 DM Dutton (1517_CR35) 1997; 12 |
| References_xml | – volume: 43 start-page: 83 issue: 1 year: 2017 end-page: 90 ident: CR11 article-title: Shear-wave elastography: could it be helpful for the diagnosis of non-mass-like breast lesions? publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2016.03.022 – volume: 6 start-page: 861 issue: 6 year: 1993 end-page: 867 ident: CR47 article-title: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function publication-title: Neural Netw doi: 10.1016/S0893-6080(05)80131-5 – ident: CR97 – year: 2017 ident: CR71 article-title: Automated detection of masses on whole breast volume ultrasound scanner: false positive reduction using deep convolutional neural network publication-title: Med Imaging – start-page: 253 year: 2014 end-page: 261 ident: CR122 publication-title: Classification and retrieval of focal and diffuse liver from ultrasound images using machine learning techniques – ident: CR51 – volume: 205 start-page: W56 issue: 1 year: 2015 end-page: W66 ident: CR24 article-title: Contrast-enhanced ultrasound of focal liver lesions publication-title: Am J Roentgenol doi: 10.2214/AJR.14.14203 – volume: 9 start-page: e108337 issue: 10 year: 2014 ident: CR20 article-title: Changes in morphological and elastic properties of patellar tendon in athletes with unilateral patellar tendinopathy and their relationships with pain and functional disability publication-title: PLoS ONE doi: 10.1371/journal.pone.0108337 – volume: E97D start-page: 2947 issue: 11 year: 2014 end-page: 2954 ident: CR133 article-title: Lesion type classification by applying machine-learning technique to contrast-enhanced ultrasound images publication-title: IEICE Trans Inf Syst doi: 10.1587/transinf.2013EDP7464 – ident: CR54 – volume: 7 start-page: 1303 issue: 5 year: 2017 ident: CR103 article-title: Ultrasound elastography: review of techniques and clinical applications publication-title: Theranostics doi: 10.7150/thno.18650 – volume: 26 start-page: S1599 year: 2015 end-page: S1611 ident: CR121 article-title: Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network publication-title: Biomed Mater Eng – volume: 21 start-page: 72 issue: 1 year: 2015 end-page: 86 ident: CR77 article-title: Learning-based prediction of gestational age from ultrasound images of the fetal brain publication-title: Med. Image Anal. doi: 10.1016/j.media.2014.12.006 – volume: 274 start-page: 821 issue: 3 year: 2014 end-page: 829 ident: CR19 article-title: Viscoelasticity in Achilles tendonopathy: quantitative assessment by using real-time shear-wave elastography publication-title: Radiology doi: 10.1148/radiol.14140434 – volume: 2 start-page: 37 issue: 1–3 year: 1987 end-page: 52 ident: CR41 article-title: Principal component analysis publication-title: Chemom Intell Lab Syst doi: 10.1016/0169-7439(87)80084-9 – volume: 44 start-page: 76 year: 2014 end-page: 87 ident: CR81 article-title: An efficient neural network based method for medical image segmentation publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2013.10.029 – ident: CR101 – volume: 37 start-page: 780 issue: 2 year: 1999 end-page: 795 ident: CR39 article-title: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/36.752194 – volume: 2 start-page: 18 issue: 3 year: 2002 end-page: 22 ident: CR42 article-title: Classification and regression by randomForest publication-title: R News – volume: 180 start-page: 689 issue: 3 year: 2017 end-page: 722 ident: CR113 article-title: Interpretable classification models for recidivism prediction publication-title: J R Stat Soc Ser A doi: 10.1111/rssa.12227 – year: 2016 ident: CR82 article-title: 3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework publication-title: Med Imaging – ident: CR57 – volume: 37 start-page: 339 issue: 1 year: 2009 end-page: 351 ident: CR120 article-title: Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE: nonlinear dimension reduction and representation in breast CADx publication-title: Med Phys doi: 10.1118/1.3267037 – volume: 20 start-page: 273 issue: 3 year: 1995 end-page: 297 ident: CR43 article-title: Support vector machine publication-title: Mach Learn – volume: 38 start-page: 262 issue: 2 year: 2012 end-page: 275 ident: CR117 article-title: Completely automated segmentation approach for breast ultrasound images using multiple-domain features publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2011.10.022 – volume: 17 start-page: 587 issue: 6 year: 2013 end-page: 600 ident: CR83 article-title: A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images publication-title: Med Image Anal doi: 10.1016/j.media.2013.04.001 – start-page: 67 year: 2017 end-page: 73 ident: CR13 article-title: Elastography: applications and limitations of a new technology publication-title: Advanced thyroid and parathyroid ultrasound doi: 10.1007/978-3-319-44100-9_8 – volume: 15 start-page: 327 issue: 1 year: 2013 end-page: 357 ident: CR65 article-title: Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev-bioeng-071812-152416 – volume: 25 start-page: 987 issue: 8 year: 2006 end-page: 1010 ident: CR79 article-title: Ultrasound image segmentation: a survey publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2006.877092 – volume: 7 start-page: 1947 issue: 1 year: 2017 ident: CR22 article-title: Accuracy of shear wave elastography for the diagnosis of prostate cancer: a meta-analysis publication-title: Sci Rep doi: 10.1038/s41598-017-02187-0 – volume: 2016 start-page: 13 year: 2016 ident: CR119 article-title: Application of artificial neural network models in segmentation and classification of nodules in breast ultrasound digital images publication-title: Int J Biomed Imaging doi: 10.1155/2016/7987212 – volume: 2 start-page: 359 issue: 5 year: 1989 end-page: 366 ident: CR46 article-title: Multilayer feedforward networks are universal approximators publication-title: Neural Netw doi: 10.1016/0893-6080(89)90020-8 – ident: CR91 – volume: 16 start-page: 933 issue: 5 year: 2012 end-page: 951 ident: CR1 article-title: Machine learning and radiology publication-title: Med Image Anal doi: 10.1016/j.media.2012.02.005 – volume: 206 start-page: 609 issue: 3 year: 2016 end-page: 616 ident: CR12 article-title: Reliability of shear-wave elastography estimates of the young modulus of tissue in follicular thyroid neoplasms publication-title: Am J Roentgenol doi: 10.2214/AJR.15.14676 – volume: 18 start-page: 103 issue: 1 year: 2014 end-page: 117 ident: CR128 article-title: Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound publication-title: Med Image Anal doi: 10.1016/j.media.2013.10.002 – year: 2012 ident: CR38 publication-title: Foundations of machine learning – volume: 44 start-page: 580 issue: 9 year: 2016 end-page: 586 ident: CR85 article-title: Diagnostic accuracy of hepatorenal index in the detection and grading of hepatic steatosis: factors affecting the accuracy of HRI publication-title: J Clin Ultrasound doi: 10.1002/jcu.22382 – volume: 34 start-page: 136 issue: 2 year: 2017 end-page: 141 ident: CR86 article-title: High frequency ultrasound: a novel instrument to quantify granuloma burden in cutaneous sarcoidosis publication-title: Sarcoidosis Vasc Diffuse Lung Dis – volume: 274 start-page: 888 issue: 3 year: 2014 end-page: 896 ident: CR8 article-title: Shear-wave elastography for the estimation of liver fibrosis in chronic liver disease: determining accuracy and ideal site for measurement publication-title: Radiology doi: 10.1148/radiol.14140839 – volume: 194 start-page: 87 year: 2016 end-page: 94 ident: CR114 article-title: Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.01.074 – volume: 27 start-page: 546 issue: 4 year: 2017 end-page: 552 ident: CR70 article-title: A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment publication-title: Thyroid doi: 10.1089/thy.2016.0372 – volume: 81 start-page: 800 issue: 4 year: 2012 end-page: 805 ident: CR14 article-title: An improved quantitative measurement for thyroid cancer detection based on elastography publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2011.01.110 – volume: 164 start-page: 92 year: 2017 end-page: 102 ident: CR92 article-title: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound publication-title: Comput Vis Image Underst doi: 10.1016/j.cviu.2017.04.002 – volume: 28 start-page: 576 issue: 5 year: 2015 end-page: 585 ident: CR116 article-title: An artificial immune system-based support vector machine approach for classifying ultrasound breast tumor images publication-title: J Digit Imaging doi: 10.1007/s10278-014-9757-1 – volume: 22 start-page: 511 issue: 2 year: 2010 end-page: 538 ident: CR58 article-title: Convolutional networks can learn to generate affinity graphs for image segmentation publication-title: Neural Comput doi: 10.1162/neco.2009.10-08-881 – volume: 42 start-page: 980 issue: 4 year: 2016 end-page: 988 ident: CR66 article-title: Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.11.016 – year: 1993 ident: CR48 publication-title: Predictive inference: an introduction doi: 10.1007/978-1-4899-4467-2 – ident: CR69 – ident: CR94 – year: 1997 ident: CR33 publication-title: Machine learning. WCB – start-page: 292 year: 2016 end-page: 301 ident: CR93 article-title: Registration of CT and ultrasound images of the spine with neural network and orientation code mutual information publication-title: Medical imaging and augmented reality doi: 10.1007/978-3-319-43775-0_26 – volume: 37 start-page: 855 issue: 3 year: 2017 end-page: 870 ident: CR18 article-title: Shear-wave elastography: basic physics and musculoskeletal applications publication-title: RadioGraphics doi: 10.1148/rg.2017160116 – volume: 16 start-page: 555 issue: 5 year: 2003 end-page: 559 ident: CR56 article-title: Subject independent facial expression recognition with robust face detection using a convolutional neural network publication-title: Neural Netw doi: 10.1016/S0893-6080(03)00115-1 – ident: CR3 – volume: 14 start-page: 1137 year: 1995 end-page: 1145 ident: CR60 article-title: A study of cross-validation and bootstrap for accuracy estimation and model selection publication-title: Ijcai – ident: CR52 – volume: 42 start-page: 1671 issue: 7 year: 2016 end-page: 1680 ident: CR108 article-title: Feasibility study of texture analysis using ultrasound shear wave elastography to predict malignancy in thyroid nodules publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2016.01.013 – volume: 58 start-page: 928 issue: 5 year: 2013 end-page: 935 ident: CR7 article-title: Liver fibrosis evaluation using real-time shear wave elastography: applicability and diagnostic performance using methods without a gold standard publication-title: J Hepatol doi: 10.1016/j.jhep.2012.12.021 – volume: 41 start-page: 1287 issue: 5 year: 2015 end-page: 1293 ident: CR84 article-title: Vascularity assessment of thyroid nodules by quantitative color doppler ultrasound publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.01.001 – volume: 8 start-page: 98 issue: 1 year: 1997 end-page: 113 ident: CR55 article-title: Face recognition: a convolutional neural-network approach publication-title: IEEE Trans Neural Netw doi: 10.1109/72.554195 – volume: 3 start-page: 535 issue: 5 year: 1990 end-page: 549 ident: CR45 article-title: Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings publication-title: Neural Netw doi: 10.1016/0893-6080(90)90004-5 – volume: 16 start-page: 215 issue: 5 year: 1992 end-page: 225 ident: CR64 article-title: Using an artificial neural network to diagnose hepatic masses publication-title: J Med Syst doi: 10.1007/BF01000274 – start-page: 63 year: 2017 end-page: 73 ident: CR76 article-title: Deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound images publication-title: Medical image understanding and analysis doi: 10.1007/978-3-319-60964-5_6 – volume: 72 start-page: 150 year: 2016 end-page: 157 ident: CR111 article-title: Deep learning based classification of breast tumors with shear-wave elastography publication-title: Ultrasonics doi: 10.1016/j.ultras.2016.08.004 – start-page: 3 year: 1983 end-page: 23 ident: CR68 article-title: An overview of machine learning publication-title: Machine learning – volume: 42 start-page: 742 issue: 3 year: 2016 end-page: 752 ident: CR131 article-title: Ultrasound image discrimination between benign and malignant adnexal masses based on a neural network approach publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.11.014 – volume: 21 start-page: 48 issue: 1 year: 2017 end-page: 55 ident: CR72 article-title: A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2016.2631401 – ident: CR87 – volume: 4 start-page: 40 year: 2010 end-page: 79 ident: CR61 article-title: A survey of cross-validation procedures for model selection publication-title: Stat Surv doi: 10.1214/09-SS054 – volume: 21 start-page: 4 issue: 1 year: 2017 end-page: 21 ident: CR4 article-title: Deep Learning for health informatics publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2016.2636665 – volume: 40 start-page: 275 issue: 2 year: 2014 end-page: 286 ident: CR107 article-title: Computer-aided diagnosis based on quantitative elastographic features with supersonic shear wave imaging publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2013.09.032 – volume: 30 start-page: 234 issue: 2 year: 2017 end-page: 243 ident: CR73 article-title: Transfer learning with convolutional neural networks for classification of abdominal ultrasound images publication-title: J Digit Imaging doi: 10.1007/s10278-016-9929-2 – volume: 81 start-page: e31 issue: 1 year: 2012 end-page: e36 ident: CR106 article-title: Real-time elastography with a novel quantitative technology for assessment of liver fibrosis in chronic hepatitis B publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2010.12.013 – volume: 521 start-page: 436 issue: 7553 year: 2015 end-page: 444 ident: CR49 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – start-page: 883 year: 2014 end-page: 885 ident: CR37 article-title: Weakly supervised learning” publication-title: Computer vision doi: 10.1007/978-0-387-31439-6_308 – volume: 15 start-page: 267 issue: 4 year: 1993 end-page: 285 ident: CR63 article-title: Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis publication-title: Ultrason Imaging doi: 10.1177/016173469301500401 – volume: 29 start-page: 1844 issue: 11 year: 2016 end-page: 1852 ident: CR125 article-title: Detection of abnormalities in ultrasound lung image using multi-level RVM classification publication-title: J Matern Fetal Neonatal Med – volume: 29 start-page: 499 issue: 5 year: 2008 end-page: 505 ident: CR26 article-title: Contrast-enhanced ultrasound for the characterization of focal liver lesions–diagnostic accuracy in clinical practice (DEGUM multicenter trial) publication-title: Ultraschall Med Stuttg Ger doi: 10.1055/s-2008-1027806 – volume: 19 start-page: 221 year: 2017 end-page: 248 ident: CR2 article-title: Deep learning in medical image analysis publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev-bioeng-071516-044442 – volume: 83 start-page: 646 issue: 4 year: 2014 end-page: 653 ident: CR30 article-title: Real-time contrast enhanced ultrasound imaging of focal splenic lesions publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2014.01.011 – volume: 41 start-page: 588 issue: 2 year: 2015 end-page: 600 ident: CR110 article-title: Quantification of elastic heterogeneity using contourlet-based texture analysis in shear-wave elastography for breast tumor classification publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2014.09.003 – volume: 30 start-page: 22 issue: 1 year: 2015 end-page: 27 ident: CR16 article-title: Shear wave elastography of passive skeletal muscle stiffness: influences of sex and age throughout adulthood publication-title: Clin Biomech doi: 10.1016/j.clinbiomech.2014.11.011 – volume: 12 start-page: 341 issue: 4 year: 1997 end-page: 367 ident: CR35 article-title: A review of machine learning publication-title: Knowl Eng Rev doi: 10.1017/S026988899700101X – ident: CR96 – ident: CR75 – ident: CR50 – volume: 10 start-page: 1493 issue: 9 year: 2015 end-page: 1503 ident: CR126 article-title: Ultrasound texture-based CAD system for detecting neuromuscular diseases publication-title: Int J Comput Assist Radiol Surg doi: 10.1007/s11548-014-1133-6 – volume: 61 start-page: 550 issue: 3 year: 2014 end-page: 557 ident: CR5 article-title: Non-invasive assessment of liver fibrosis with impulse elastography: comparison of Supersonic Shear Imaging with ARFI and FibroScan publication-title: J. Hepatol. doi: 10.1016/j.jhep.2014.04.044 – volume: 35 start-page: 2501 issue: 11 year: 2016 end-page: 2509 ident: CR135 article-title: Image segmentation and machine learning for detection of abdominal free fluid in focused assessment with sonography for trauma examinations: a pilot study publication-title: J Ultrasound Med doi: 10.7863/ultra.15.11017 – volume: 42 start-page: 835 issue: 4 year: 2016 end-page: 847 ident: CR9 article-title: Breast lesions: quantitative diagnosis using ultrasound shear wave elastography—a systematic review and meta-analysis publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.10.024 – volume: 84 start-page: 407 issue: 3 year: 2015 end-page: 412 ident: CR15 article-title: Shear wave elastography of thyroid nodules for the prediction of malignancy in a large scale study publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2014.11.019 – volume: 20 start-page: 3590 issue: 13 year: 2014 end-page: 3596 ident: CR25 article-title: Contrast-enhanced ultrasound in the diagnosis of nodules in liver cirrhosis publication-title: World J Gastroenterol doi: 10.3748/wjg.v20.i13.3590 – volume: 27 start-page: 1858 issue: 5 year: 2017 end-page: 1866 ident: CR21 article-title: Stiffness of benign and malignant prostate tissue measured by shear-wave elastography: a preliminary study publication-title: Eur Radiol doi: 10.1007/s00330-016-4534-9 – year: 2017 ident: CR74 article-title: A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets publication-title: Med Phys – volume: 27 start-page: 723 issue: 2 year: 2017 end-page: 731 ident: CR17 article-title: Do quantitative and qualitative shear wave elastography have a role in evaluating musculoskeletal soft tissue masses? publication-title: Eur Radiol doi: 10.1007/s00330-016-4427-y – ident: CR99 – year: 2006 ident: CR32 publication-title: Pattern recognition and machine learning – volume: 79 start-page: 358 issue: 4 year: 2012 end-page: 364 ident: CR127 article-title: Machine learning algorithms to classify spinal muscular atrophy subtypes publication-title: Neurology doi: 10.1212/WNL.0b013e3182604395 – volume: 9 start-page: 293 issue: 3 year: 1999 end-page: 300 ident: CR44 article-title: Least squares support vector machine classifiers publication-title: Neural Process Lett doi: 10.1023/A:1018628609742 – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: CR67 article-title: Deep learning in neural networks: an overview publication-title: Neural Netw doi: 10.1016/j.neunet.2014.09.003 – ident: CR95 – volume: 41 start-page: 2677 issue: 10 year: 2015 end-page: 2689 ident: CR129 article-title: Lumbar ultrasound image feature extraction and classification with support vector machine publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.05.015 – volume: 17 start-page: 1 issue: 16 year: 2013 end-page: 243 ident: CR27 article-title: Contrast-enhanced ultrasound using SonoVue (sulphur hexafluoride microbubbles) compared with contrast-enhanced computed tomography and contrast-enhanced magnetic resonance imaging for the characterisation of focal liver lesions and detection of liver metastases: a systematic review and cost-effectiveness analysis publication-title: Health Technol Assess Winch Engl – year: 2017 ident: CR31 article-title: A direct comparison of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging for prostate cancer detection and prediction of aggressiveness publication-title: Eur Radiol – volume: 125 start-page: 4057 issue: 15 year: 2014 end-page: 4063 ident: CR112 article-title: Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound publication-title: Opt-Int J Light Electron Opt doi: 10.1016/j.ijleo.2014.01.114 – ident: CR53 – volume: 21 start-page: 968 issue: 3 year: 2012 end-page: 982 ident: CR88 article-title: The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2011.2169273 – volume: 26 start-page: 159 issue: 3 year: 2006 end-page: 190 ident: CR36 article-title: Machine learning: a review of classification and combining techniques publication-title: Artif Intell Rev doi: 10.1007/s10462-007-9052-3 – volume: 43 start-page: 1797 year: 2017 end-page: 1810 ident: CR109 article-title: A machine-learning algorithm toward color analysis for chronic liver disease classification, employing ultrasound shear wave elastography publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2017.05.002 – volume: 28 start-page: 448 issue: 4 year: 2015 end-page: 458 ident: CR132 article-title: SVM-based CAC system for B-mode kidney ultrasound images publication-title: J Digit Imaging doi: 10.1007/s10278-014-9754-4 – volume: 36 start-page: 15 year: 2017 end-page: 21 ident: CR102 article-title: Automatic apical view classification of echocardiograms using a discriminative learning dictionary publication-title: Med Image Anal doi: 10.1016/j.media.2016.10.007 – year: 2014 ident: CR123 article-title: Artificial neural network application in the diagnosis of disease conditions with liver ultrasound images publication-title: Adv Bioinforma – volume: 11 start-page: 41 issue: 1 year: 2015 end-page: 45 ident: CR29 article-title: Contrast-enhanced ultrasound in the diagnosis of solitary thyroid nodules publication-title: J Cancer Res Ther doi: 10.4103/0973-1482.147382 – volume: 25 start-page: 99 issue: 1–2 year: 1998 end-page: 123 ident: CR34 article-title: Machine learning from examples: inductive and lazy methods publication-title: Data Knowl Eng doi: 10.1016/S0169-023X(97)00053-0 – ident: CR40 – volume: 143 start-page: 29 issue: 1 year: 1982 end-page: 36 ident: CR62 article-title: The meaning and use of the area under a receiver operating characteristic (ROC) curve publication-title: Radiology doi: 10.1148/radiology.143.1.7063747 – ident: CR98 – volume: 41 start-page: 022901 issue: 2 year: 2014 ident: CR78 article-title: Brachial artery vasomotion and transducer pressure effect on measurements by active contour segmentation on ultrasound: brachial artery vasomotion and transducer pressure effect publication-title: Med Phys doi: 10.1118/1.4862508 – volume: 224 start-page: 307 issue: 2 year: 2010 end-page: 316 ident: CR80 article-title: Ultrasound image segmentation and tissue characterization publication-title: Proc Inst Mech Eng Part H doi: 10.1243/09544119JEIM604 – volume: 209 start-page: 1 year: 2017 end-page: 9 ident: CR23 article-title: Shear-wave elastography for detection of prostate cancer: a systematic review and diagnostic meta-analysis publication-title: Am J Roentgenol doi: 10.2214/AJR.17.18056 – ident: CR90 – volume: 32 start-page: 283 issue: 3 year: 2016 end-page: 292 ident: CR118 article-title: Breast tumor classification in ultrasound images using support vector machines and neural networks publication-title: Rev Bras Eng Biomed – ident: CR130 – volume: 36 start-page: 103 year: 2017 end-page: 113 ident: CR100 article-title: A fused deep learning architecture for viewpoint classification of echocardiography publication-title: Inf Fusion doi: 10.1016/j.inffus.2016.11.007 – volume: 33 start-page: 197 issue: 2 year: 2014 end-page: 203 ident: CR6 article-title: Shear wave elastography for evaluation of liver fibrosis publication-title: J. Ultrasound Med doi: 10.7863/ultra.33.2.197 – volume: 43 start-page: 601 issue: 3 year: 2017 end-page: 606 ident: CR10 article-title: Differential diagnosis of breast category 3 and 4 nodules through BI-RADS classification in conjunction with shear wave elastography publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2016.10.004 – volume: 55 start-page: 587 issue: 9 year: 2014 end-page: 592 ident: CR28 article-title: Diagnostic efficacy of contrast-enhanced ultrasound for small renal masses publication-title: Korean J Urol doi: 10.4111/kju.2014.55.9.587 – volume: 151 start-page: 161 issue: P1 year: 2015 end-page: 167 ident: CR89 article-title: Fully automatic segmentation of ultrasound common carotid artery images based on machine learning publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.09.066 – volume: 59 start-page: 1729 year: 2012 end-page: 1740 ident: CR104 article-title: Parameters affecting the resolution and accuracy of 2-D quantitative shear wave images publication-title: IEEE Trans Ultrason Ferroelectr Freq Control doi: 10.1109/TUFFC.2012.2377 – ident: CR59 – volume: 70 start-page: 1221 year: 2015 end-page: 1224 ident: CR134 article-title: Automatic cataract classification based on ultrasound technique using machine learning: a comparative study publication-title: Phys Procedia doi: 10.1016/j.phpro.2015.08.263 – volume: 32 start-page: 2185 issue: 12 year: 2013 end-page: 2190 ident: CR124 article-title: Automated B-line scoring on thoracic sonography publication-title: J Ultrasound Med doi: 10.7863/ultra.32.12.2185 – volume: 41 start-page: 936 issue: 4 year: 2015 end-page: 943 ident: CR105 article-title: Automatic assessment of shear wave elastography quality and measurement reliability in the liver publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2014.11.010 – volume: 66 start-page: 114 year: 2016 end-page: 123 ident: CR115 article-title: Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2016.09.006 – volume: 274 start-page: 821 issue: 3 year: 2014 ident: 1517_CR19 publication-title: Radiology doi: 10.1148/radiol.14140434 – volume: 83 start-page: 646 issue: 4 year: 2014 ident: 1517_CR30 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2014.01.011 – volume: 32 start-page: 2185 issue: 12 year: 2013 ident: 1517_CR124 publication-title: J Ultrasound Med doi: 10.7863/ultra.32.12.2185 – volume: 34 start-page: 136 issue: 2 year: 2017 ident: 1517_CR86 publication-title: Sarcoidosis Vasc Diffuse Lung Dis – volume: 28 start-page: 576 issue: 5 year: 2015 ident: 1517_CR116 publication-title: J Digit Imaging doi: 10.1007/s10278-014-9757-1 – volume: 10 start-page: 1493 issue: 9 year: 2015 ident: 1517_CR126 publication-title: Int J Comput Assist Radiol Surg doi: 10.1007/s11548-014-1133-6 – volume: 151 start-page: 161 issue: P1 year: 2015 ident: 1517_CR89 publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.09.066 – volume: 274 start-page: 888 issue: 3 year: 2014 ident: 1517_CR8 publication-title: Radiology doi: 10.1148/radiol.14140839 – volume: 15 start-page: 327 issue: 1 year: 2013 ident: 1517_CR65 publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev-bioeng-071812-152416 – volume: 21 start-page: 72 issue: 1 year: 2015 ident: 1517_CR77 publication-title: Med. Image Anal. doi: 10.1016/j.media.2014.12.006 – ident: 1517_CR97 – year: 2017 ident: 1517_CR31 publication-title: Eur Radiol doi: 10.1007/s00330-017-5192-2 – ident: 1517_CR54 – start-page: 253 volume-title: Classification and retrieval of focal and diffuse liver from ultrasound images using machine learning techniques year: 2014 ident: 1517_CR122 – volume: 43 start-page: 601 issue: 3 year: 2017 ident: 1517_CR10 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2016.10.004 – volume: 26 start-page: 159 issue: 3 year: 2006 ident: 1517_CR36 publication-title: Artif Intell Rev doi: 10.1007/s10462-007-9052-3 – ident: 1517_CR51 doi: 10.1109/CVPR.2015.7298594 – volume: 9 start-page: e108337 issue: 10 year: 2014 ident: 1517_CR20 publication-title: PLoS ONE doi: 10.1371/journal.pone.0108337 – volume: 22 start-page: 511 issue: 2 year: 2010 ident: 1517_CR58 publication-title: Neural Comput doi: 10.1162/neco.2009.10-08-881 – volume: 3 start-page: 535 issue: 5 year: 1990 ident: 1517_CR45 publication-title: Neural Netw doi: 10.1016/0893-6080(90)90004-5 – volume: 12 start-page: 341 issue: 4 year: 1997 ident: 1517_CR35 publication-title: Knowl Eng Rev doi: 10.1017/S026988899700101X – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 1517_CR43 publication-title: Mach Learn – volume: 2 start-page: 359 issue: 5 year: 1989 ident: 1517_CR46 publication-title: Neural Netw doi: 10.1016/0893-6080(89)90020-8 – start-page: 883 volume-title: Computer vision year: 2014 ident: 1517_CR37 doi: 10.1007/978-0-387-31439-6_308 – volume: 205 start-page: W56 issue: 1 year: 2015 ident: 1517_CR24 publication-title: Am J Roentgenol doi: 10.2214/AJR.14.14203 – volume: 8 start-page: 98 issue: 1 year: 1997 ident: 1517_CR55 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.554195 – volume: 81 start-page: e31 issue: 1 year: 2012 ident: 1517_CR106 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2010.12.013 – volume: 79 start-page: 358 issue: 4 year: 2012 ident: 1517_CR127 publication-title: Neurology doi: 10.1212/WNL.0b013e3182604395 – volume: 19 start-page: 221 year: 2017 ident: 1517_CR2 publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev-bioeng-071516-044442 – volume: 206 start-page: 609 issue: 3 year: 2016 ident: 1517_CR12 publication-title: Am J Roentgenol doi: 10.2214/AJR.15.14676 – volume: 16 start-page: 215 issue: 5 year: 1992 ident: 1517_CR64 publication-title: J Med Syst doi: 10.1007/BF01000274 – volume: 81 start-page: 800 issue: 4 year: 2012 ident: 1517_CR14 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2011.01.110 – ident: 1517_CR40 – volume: 224 start-page: 307 issue: 2 year: 2010 ident: 1517_CR80 publication-title: Proc Inst Mech Eng Part H doi: 10.1243/09544119JEIM604 – ident: 1517_CR3 doi: 10.1016/j.media.2017.07.005 – start-page: 67 volume-title: Advanced thyroid and parathyroid ultrasound year: 2017 ident: 1517_CR13 doi: 10.1007/978-3-319-44100-9_8 – volume: 43 start-page: 1797 year: 2017 ident: 1517_CR109 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2017.05.002 – volume: 40 start-page: 275 issue: 2 year: 2014 ident: 1517_CR107 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2013.09.032 – volume: 55 start-page: 587 issue: 9 year: 2014 ident: 1517_CR28 publication-title: Korean J Urol doi: 10.4111/kju.2014.55.9.587 – volume: 36 start-page: 103 year: 2017 ident: 1517_CR100 publication-title: Inf Fusion doi: 10.1016/j.inffus.2016.11.007 – ident: 1517_CR101 doi: 10.1109/ISBI.2017.7950609 – volume: 42 start-page: 980 issue: 4 year: 2016 ident: 1517_CR66 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.11.016 – volume: 143 start-page: 29 issue: 1 year: 1982 ident: 1517_CR62 publication-title: Radiology doi: 10.1148/radiology.143.1.7063747 – volume: 27 start-page: 546 issue: 4 year: 2017 ident: 1517_CR70 publication-title: Thyroid doi: 10.1089/thy.2016.0372 – start-page: 63 volume-title: Medical image understanding and analysis year: 2017 ident: 1517_CR76 doi: 10.1007/978-3-319-60964-5_6 – volume: 33 start-page: 197 issue: 2 year: 2014 ident: 1517_CR6 publication-title: J. Ultrasound Med doi: 10.7863/ultra.33.2.197 – volume: 17 start-page: 587 issue: 6 year: 2013 ident: 1517_CR83 publication-title: Med Image Anal doi: 10.1016/j.media.2013.04.001 – volume: 29 start-page: 1844 issue: 11 year: 2016 ident: 1517_CR125 publication-title: J Matern Fetal Neonatal Med – volume: 41 start-page: 588 issue: 2 year: 2015 ident: 1517_CR110 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2014.09.003 – volume: 58 start-page: 928 issue: 5 year: 2013 ident: 1517_CR7 publication-title: J Hepatol doi: 10.1016/j.jhep.2012.12.021 – volume: 2 start-page: 37 issue: 1–3 year: 1987 ident: 1517_CR41 publication-title: Chemom Intell Lab Syst doi: 10.1016/0169-7439(87)80084-9 – ident: 1517_CR90 doi: 10.1109/BIBM.2016.7822557 – volume: 72 start-page: 150 year: 2016 ident: 1517_CR111 publication-title: Ultrasonics doi: 10.1016/j.ultras.2016.08.004 – volume: 15 start-page: 267 issue: 4 year: 1993 ident: 1517_CR63 publication-title: Ultrason Imaging doi: 10.1177/016173469301500401 – volume: 38 start-page: 262 issue: 2 year: 2012 ident: 1517_CR117 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2011.10.022 – volume: 43 start-page: 83 issue: 1 year: 2017 ident: 1517_CR11 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2016.03.022 – ident: 1517_CR91 doi: 10.1109/ISBI.2017.7950519 – year: 2014 ident: 1517_CR123 publication-title: Adv Bioinforma doi: 10.1155/2014/708279 – volume: E97D start-page: 2947 issue: 11 year: 2014 ident: 1517_CR133 publication-title: IEICE Trans Inf Syst doi: 10.1587/transinf.2013EDP7464 – volume: 194 start-page: 87 year: 2016 ident: 1517_CR114 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.01.074 – volume: 209 start-page: 1 year: 2017 ident: 1517_CR23 publication-title: Am J Roentgenol doi: 10.2214/AJR.17.18056 – volume: 41 start-page: 022901 issue: 2 year: 2014 ident: 1517_CR78 publication-title: Med Phys doi: 10.1118/1.4862508 – volume: 42 start-page: 835 issue: 4 year: 2016 ident: 1517_CR9 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.10.024 – volume: 27 start-page: 1858 issue: 5 year: 2017 ident: 1517_CR21 publication-title: Eur Radiol doi: 10.1007/s00330-016-4534-9 – volume-title: Foundations of machine learning year: 2012 ident: 1517_CR38 – year: 2017 ident: 1517_CR74 publication-title: Med Phys doi: 10.1002/mp.12453 – volume: 61 start-page: 550 issue: 3 year: 2014 ident: 1517_CR5 publication-title: J. Hepatol. doi: 10.1016/j.jhep.2014.04.044 – volume: 2 start-page: 18 issue: 3 year: 2002 ident: 1517_CR42 publication-title: R News – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 1517_CR49 publication-title: Nature doi: 10.1038/nature14539 – volume: 14 start-page: 1137 year: 1995 ident: 1517_CR60 publication-title: Ijcai – ident: 1517_CR69 doi: 10.1109/SPMB.2016.7846873 – volume: 21 start-page: 48 issue: 1 year: 2017 ident: 1517_CR72 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2016.2631401 – volume: 44 start-page: 580 issue: 9 year: 2016 ident: 1517_CR85 publication-title: J Clin Ultrasound doi: 10.1002/jcu.22382 – volume: 42 start-page: 742 issue: 3 year: 2016 ident: 1517_CR131 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.11.014 – ident: 1517_CR96 doi: 10.1007/978-3-319-46723-8_24 – ident: 1517_CR53 – ident: 1517_CR98 doi: 10.1007/978-3-319-24553-9_62 – volume: 36 start-page: 15 year: 2017 ident: 1517_CR102 publication-title: Med Image Anal doi: 10.1016/j.media.2016.10.007 – volume: 37 start-page: 780 issue: 2 year: 1999 ident: 1517_CR39 publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/36.752194 – volume: 41 start-page: 1287 issue: 5 year: 2015 ident: 1517_CR84 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.01.001 – volume: 41 start-page: 936 issue: 4 year: 2015 ident: 1517_CR105 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2014.11.010 – volume: 164 start-page: 92 year: 2017 ident: 1517_CR92 publication-title: Comput Vis Image Underst doi: 10.1016/j.cviu.2017.04.002 – volume: 84 start-page: 407 issue: 3 year: 2015 ident: 1517_CR15 publication-title: Eur J Radiol doi: 10.1016/j.ejrad.2014.11.019 – volume: 37 start-page: 855 issue: 3 year: 2017 ident: 1517_CR18 publication-title: RadioGraphics doi: 10.1148/rg.2017160116 – ident: 1517_CR94 doi: 10.1117/12.912188 – volume: 29 start-page: 499 issue: 5 year: 2008 ident: 1517_CR26 publication-title: Ultraschall Med Stuttg Ger doi: 10.1055/s-2008-1027806 – volume: 42 start-page: 1671 issue: 7 year: 2016 ident: 1517_CR108 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2016.01.013 – volume: 30 start-page: 234 issue: 2 year: 2017 ident: 1517_CR73 publication-title: J Digit Imaging doi: 10.1007/s10278-016-9929-2 – volume: 59 start-page: 1729 year: 2012 ident: 1517_CR104 publication-title: IEEE Trans Ultrason Ferroelectr Freq Control doi: 10.1109/TUFFC.2012.2377 – year: 2017 ident: 1517_CR71 publication-title: Med Imaging doi: 10.1117/12.2254581 – volume-title: Pattern recognition and machine learning year: 2006 ident: 1517_CR32 – volume: 25 start-page: 987 issue: 8 year: 2006 ident: 1517_CR79 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2006.877092 – volume: 7 start-page: 1303 issue: 5 year: 2017 ident: 1517_CR103 publication-title: Theranostics doi: 10.7150/thno.18650 – volume: 21 start-page: 4 issue: 1 year: 2017 ident: 1517_CR4 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2016.2636665 – ident: 1517_CR50 – volume: 20 start-page: 3590 issue: 13 year: 2014 ident: 1517_CR25 publication-title: World J Gastroenterol doi: 10.3748/wjg.v20.i13.3590 – ident: 1517_CR87 – volume: 18 start-page: 103 issue: 1 year: 2014 ident: 1517_CR128 publication-title: Med Image Anal doi: 10.1016/j.media.2013.10.002 – start-page: 3 volume-title: Machine learning year: 1983 ident: 1517_CR68 – volume: 125 start-page: 4057 issue: 15 year: 2014 ident: 1517_CR112 publication-title: Opt-Int J Light Electron Opt doi: 10.1016/j.ijleo.2014.01.114 – volume: 21 start-page: 968 issue: 3 year: 2012 ident: 1517_CR88 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2011.2169273 – volume: 30 start-page: 22 issue: 1 year: 2015 ident: 1517_CR16 publication-title: Clin Biomech doi: 10.1016/j.clinbiomech.2014.11.011 – ident: 1517_CR59 doi: 10.1109/CVPR.2015.7298965 – volume: 32 start-page: 283 issue: 3 year: 2016 ident: 1517_CR118 publication-title: Rev Bras Eng Biomed – volume: 70 start-page: 1221 year: 2015 ident: 1517_CR134 publication-title: Phys Procedia doi: 10.1016/j.phpro.2015.08.263 – volume: 66 start-page: 114 year: 2016 ident: 1517_CR115 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2016.09.006 – volume-title: Machine learning. WCB year: 1997 ident: 1517_CR33 – volume: 27 start-page: 723 issue: 2 year: 2017 ident: 1517_CR17 publication-title: Eur Radiol doi: 10.1007/s00330-016-4427-y – ident: 1517_CR130 doi: 10.1109/ICRCICN.2015.7434224 – ident: 1517_CR95 doi: 10.1109/ISBI.2016.7493384 – volume: 41 start-page: 2677 issue: 10 year: 2015 ident: 1517_CR129 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.05.015 – ident: 1517_CR57 doi: 10.1145/2671188.2749408 – year: 2016 ident: 1517_CR82 publication-title: Med Imaging doi: 10.1117/12.2216396 – volume: 4 start-page: 40 year: 2010 ident: 1517_CR61 publication-title: Stat Surv doi: 10.1214/09-SS054 – ident: 1517_CR99 doi: 10.1007/978-3-319-24574-4_82 – volume: 28 start-page: 448 issue: 4 year: 2015 ident: 1517_CR132 publication-title: J Digit Imaging doi: 10.1007/s10278-014-9754-4 – ident: 1517_CR52 doi: 10.1109/CVPR.2013.465 – volume: 9 start-page: 293 issue: 3 year: 1999 ident: 1517_CR44 publication-title: Neural Process Lett doi: 10.1023/A:1018628609742 – volume: 25 start-page: 99 issue: 1–2 year: 1998 ident: 1517_CR34 publication-title: Data Knowl Eng doi: 10.1016/S0169-023X(97)00053-0 – volume: 2016 start-page: 13 year: 2016 ident: 1517_CR119 publication-title: Int J Biomed Imaging doi: 10.1155/2016/7987212 – start-page: 292 volume-title: Medical imaging and augmented reality year: 2016 ident: 1517_CR93 doi: 10.1007/978-3-319-43775-0_26 – volume: 180 start-page: 689 issue: 3 year: 2017 ident: 1517_CR113 publication-title: J R Stat Soc Ser A doi: 10.1111/rssa.12227 – volume: 17 start-page: 1 issue: 16 year: 2013 ident: 1517_CR27 publication-title: Health Technol Assess Winch Engl – volume: 26 start-page: S1599 year: 2015 ident: 1517_CR121 publication-title: Biomed Mater Eng – volume: 16 start-page: 555 issue: 5 year: 2003 ident: 1517_CR56 publication-title: Neural Netw doi: 10.1016/S0893-6080(03)00115-1 – volume: 37 start-page: 339 issue: 1 year: 2009 ident: 1517_CR120 publication-title: Med Phys doi: 10.1118/1.3267037 – volume: 7 start-page: 1947 issue: 1 year: 2017 ident: 1517_CR22 publication-title: Sci Rep doi: 10.1038/s41598-017-02187-0 – volume: 6 start-page: 861 issue: 6 year: 1993 ident: 1517_CR47 publication-title: Neural Netw doi: 10.1016/S0893-6080(05)80131-5 – volume-title: Predictive inference: an introduction year: 1993 ident: 1517_CR48 doi: 10.1007/978-1-4899-4467-2 – ident: 1517_CR75 doi: 10.1007/978-3-319-60964-5_9 – volume: 35 start-page: 2501 issue: 11 year: 2016 ident: 1517_CR135 publication-title: J Ultrasound Med doi: 10.7863/ultra.15.11017 – volume: 44 start-page: 76 year: 2014 ident: 1517_CR81 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2013.10.029 – volume: 61 start-page: 85 year: 2015 ident: 1517_CR67 publication-title: Neural Netw doi: 10.1016/j.neunet.2014.09.003 – volume: 16 start-page: 933 issue: 5 year: 2012 ident: 1517_CR1 publication-title: Med Image Anal doi: 10.1016/j.media.2012.02.005 – volume: 11 start-page: 41 issue: 1 year: 2015 ident: 1517_CR29 publication-title: J Cancer Res Ther doi: 10.4103/0973-1482.147382 |
| SSID | ssj0001651125 ssj0008379 |
| Score | 2.5867848 |
| SecondaryResourceType | review_article |
| Snippet | Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing,... |
| SourceID | pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 786 |
| SubjectTerms | Abdomen - diagnostic imaging Artificial intelligence Computation Computer applications Deep learning Diagnostic systems Forecasting Gastroenterology Hepatology Humans Image acquisition Image enhancement Image processing Image quality Imaging Invited Article Learning algorithms Machine Learning Medicine Medicine & Public Health Miniaturization Quality control Radiology Ultrasonic imaging Ultrasonic testing Ultrasonography - methods Ultrasound Workflow |
| SummonAdditionalLinks | – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3da9RAEB-0QvGl-FE1tcoKPqnBZL-S-CIiHodwPoiFewu7m01buObO3uX_78xmk3IW-5aQzcfOx87MzuQ3AO8LtGmtbWTKbWNSyb1OjfBtWgjhuXfoJAQkpsUvPT-TP5dqGTfctrGsclwTw0LdrB3tkX8O6ECUBSu-bv6m1DWKsquxhcZDeETQZVTSVSyngAu9i4C1x4XGqDmTyzGrmQUQUdp5yXKMoRQu1Nm-XbrjbN6tmfwncRrs0ewJHEVHkn0bOP8UHvjuGRwuYqr8OfxehDJJz2JfiHOG7im7GvIyrF_h47fUUukLo3-K-u0nNjSTxgPTNWzAGmHrDfnnfRdwV4_hbPbjz_d5GhsopE4JvUtdZTH4Faa0aJh92bauNMrwyuSeEy6Ozk2RowVHrVZWVI203HvhGt7imTROvICDbt35V8CKUljZoPI6J6VX2rZV5Ty3TjtqEVMlkI3kq11EF6cmF6t6wkUOFK-R4jVRvM4S-DDdshmgNe4bfDrypI5atq1vZSKBd9Nl1A9KepjOr_thjNKlKmQCLwcWTm_jFcElZiqBYo-50wDC3t6_0l1eBAxuVZa6zPMEPo5icPtZ_53Eyf2TeA2POQlkqAs6hYPdde_foMuzs2-DXN8Aef_9Fw priority: 102 providerName: ProQuest |
| Title | Machine learning for medical ultrasound: status, methods, and future opportunities |
| URI | https://link.springer.com/article/10.1007/s00261-018-1517-0 https://www.ncbi.nlm.nih.gov/pubmed/29492605 https://www.proquest.com/docview/2009235017 https://www.proquest.com/docview/2009568574 https://pubmed.ncbi.nlm.nih.gov/PMC5886811 |
| Volume | 43 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Zb9QwEB7BVkK8cB-BdmUknoBUic-Et27VbQXaFapaaXmKbMcpiJKt2M1Lf33HzgHbAlJfcshO4mNsf5MZfwPwVuGaVpmSx9SUOubUyVgzV8WKMUedRZAQmJhmc3l0yj8txKLbx73qvd17k2SYqYfNbkFdQNUXtR6BUyvq6VsIPxI-gq29w6-f__i1Ij2KECGsnER9OeGL3p75t_dsrkg3YOZNb8lrJtOwEk0fwklfh9YB5cdusza79vIaveMtK_kIHnTIlOy1ovQY7rj6Cdybdbb3p3A8C36XjnSBJs4I4l3yszX0kOYcS73yMZo-Er9JqVl9IG10arzQdUla8hKyvPCAv6kDkeszOJ0enOwfxV1EhtgKJtexzQ1q00xnBld6l1WVzbTQNNepo55oR6ZapQgJcJoQhuUlN9Q5Zkta4R3Xlj2HUb2s3UsgKmOGlzgbWMu5E9JUeW4dNVZaH3MmjyDpe6WwHV25j5pxXgxEy6GtCmyrwrdVkUTwbnjkouXq-F_m7b6ri27YropAQeVNrSqCN0MyDjhvRdG1WzZtHiEzoXgEL1rJGL5Gc8-_mIgI1IbMDBk8mfdmSv39WyD1FlkmszSN4H0vGL-L9c9KvLpV7tdwn3rJCn5H2zBa_2rcDkKqtRnDXbVQeMymh2McTtPJZD7uhhWeJwfzL8dXxyEb1g |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VIgEXBOUVKGCkcgEiEr_iICGEoMv2sT2gVtpbajtOi1SyC7sR4k_xGxk7j2qp6K23RHGceDxPj_0NwFaGNq0yJY-pKXXMqZOxZq6KM8YcdRadhIDENDmQ4yO-OxXTNfjTn4Xx2yp7nRgUdTmzfo38bUAH8lmw7MP8R-yrRvnsal9Co2WLPff7F4Zsi_c7n3F-X1I62j78NI67qgKxFUwuY5sbjAiZVgatlVNVZZUWmuY6ddSDxchUZymaNWR1YVheckOdY7akFd5xbRn2ew2uc8ZyL1Fq9GXQ_KrF9qNMYpSe8GmfRU0CaKlf6UlSjNkEGoZk1Q5ecG4v7tH8J1Eb7N_oDtzuHFfyseW0u7Dm6g24MelS8_fg6yRsy3Skq0NxQtAdJt_bPBBpzrD7hS_h9I74M0zN4g1pi1fjha5L0mKbkNncxwNNHXBe78PRlZD2AazXs9o9ApIpZniJysJazp2Qpspz66ix0vqSNHkESU--wnZo5r6oxlkx4DAHihdI8cJTvEgieDW8Mm-hPC5rvNnPSdFJ9aI458EIXgyPUR59kkXXbta0bYRUIuMRPGyncPgazT08YyIiyFYmd2jgsb5Xn9TfTgPmt1BKqjSN4HXPBue_9d9BPL58EM_h5vhwsl_s7xzsPYFb1DNn2JO0CevLn417iu7W0jwLPE7g-KqF6i-4ojnG |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9QwDLfGkCZeEN8UBgQJXoBqbT6aFgkhxDhtjJsQYtK9dUmaAtLoHdxViH-Nvw47_ZiOib3trVXTtHHs2I6dnwGeaNRpta1kzG1lYsl9Fhvh61gL4bl3aCQEJKbpYbZ3JN_P1GwD_gxnYSitclgTw0JdzR3tke8EdCCKgumduk-L-Lg7eb34EVMFKYq0DuU0OhY58L9_ofu2fLW_i3P9lPPJu89v9-K-wkDslMhWsSsseofC5BY1l8_r2uVGGV6Y1HMCjslSo1NUccj2yoqikpZ7L1zFa7yTxgns9xJc1kKlJGN6Njp7aNkEnD8uMvTYEzkbIqpJADClXZ8kRf9NoZJI1nXiGUP3bL7mP0HboAsn1-Bqb8SyNx3XXYcN39yArWkfpr8Jn6YhRdOzvibFF4amMfvexYRYe4LdL6mc00tG55na5QvWFbLGC9NUrMM5YfMF-QZtEzBfb8HRhZD2Nmw288bfBaZzYWWFC4dzUnqV2boonOfWZY7K0xQRJAP5Stcjm1OBjZNyxGQOFC-R4iVRvEwieDa-suhgPc5rvD3MSdlL-LI85ccIHo-PUTYp4GIaP2-7NirLlZYR3OmmcPwaLwiqMVER6LXJHRsQ7vf6k-bb14D_rfI8y9M0gucDG5z-1n8Hce_8QTyCLRSn8sP-4cF9uMKJN0N60jZsrn62_gFaXiv7MLA4g-OLlqm_SaY98w |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+learning+for+medical+ultrasound%3A+status%2C+methods%2C+and+future+opportunities&rft.jtitle=Abdominal+radiology+%28New+York%29&rft.au=Brattain%2C+Laura+J&rft.au=Telfer%2C+Brian+A&rft.au=Dhyani%2C+Manish&rft.au=Grajo%2C+Joseph+R&rft.date=2018-04-01&rft.issn=2366-0058&rft.eissn=2366-0058&rft.volume=43&rft.issue=4&rft.spage=786&rft_id=info:doi/10.1007%2Fs00261-018-1517-0&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2366-004X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2366-004X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2366-004X&client=summon |