Preparing Medical Imaging Data for Machine Learning
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementat...
Saved in:
| Published in | Radiology Vol. 295; no. 1; pp. 4 - 15 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Radiological Society of North America
01.04.2020
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability. |
|---|---|
| AbstractList | Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability. Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability. © RSNA, 2020 Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability. |
| Author | Willemink, Martin J. Harvey, Hugh Wu, Jie Koszek, Wojciech A. Rubin, Daniel L. Summers, Ronald M. Lungren, Matthew P. Fleischmann, Dominik Hardell, Cailin Folio, Les R. |
| Author_xml | – sequence: 1 givenname: Martin J. orcidid: 0000-0002-6991-6557 surname: Willemink fullname: Willemink, Martin J. – sequence: 2 givenname: Wojciech A. orcidid: 0000-0002-8949-8858 surname: Koszek fullname: Koszek, Wojciech A. – sequence: 3 givenname: Cailin surname: Hardell fullname: Hardell, Cailin – sequence: 4 givenname: Jie orcidid: 0000-0003-3564-9056 surname: Wu fullname: Wu, Jie – sequence: 5 givenname: Dominik orcidid: 0000-0003-0715-0952 surname: Fleischmann fullname: Fleischmann, Dominik – sequence: 6 givenname: Hugh orcidid: 0000-0003-4528-1312 surname: Harvey fullname: Harvey, Hugh – sequence: 7 givenname: Les R. surname: Folio fullname: Folio, Les R. – sequence: 8 givenname: Ronald M. orcidid: 0000-0001-8081-7376 surname: Summers fullname: Summers, Ronald M. – sequence: 9 givenname: Daniel L. surname: Rubin fullname: Rubin, Daniel L. – sequence: 10 givenname: Matthew P. orcidid: 0000-0002-8591-5861 surname: Lungren fullname: Lungren, Matthew P. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32068507$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1UclOwzAQtVARXeADuKAcuaSMl8TOBQmxVmoFBzhbtuO0RqldnBSJvydVWzaJ02hm3jKaN0Q9H7xF6BTDGGMmLqIqXajHBAjgghDCDtAAZ4SnmOKshwYAlKaC4aKPhk3zCoBZJvgR6lMCuciADxB9inalovPzZGZLZ1SdTJZqvulvVKuSKsRkpszCeZtMrYq-2xyjw0rVjT3Z1RF6ubt9vn5Ip4_3k-uraWpoUbSpKbOSg2VCg2FKCw4VoxXBQC1owQqic62BZnmFTVl2Z1YFzY0WwLszS2PpCF1udVdrvbTdxLdR1XIV3VLFDxmUk7833i3kPLxLjoFxwJ3A-U4ghre1bVq5dI2xda28DetGEppxxnLBeAc9--n1ZbL_VAfgW4CJoWmiraRxrWpd2Fi7WmKQm0zkNhP5nUnHxH-Ye_H_OZ-l_o_i |
| CitedBy_id | crossref_primary_10_1148_ryai_2021210097 crossref_primary_10_1093_bjro_tzad004 crossref_primary_10_3390_jpm12091471 crossref_primary_10_1016_j_ejmp_2021_03_009 crossref_primary_10_3233_JIFS_221386 crossref_primary_10_1016_j_xops_2022_100198 crossref_primary_10_1177_08465371241236376 crossref_primary_10_1002_acm2_13662 crossref_primary_10_1055_s_0044_1800860 crossref_primary_10_1016_j_slast_2024_100218 crossref_primary_10_1007_s11263_024_02145_0 crossref_primary_10_2139_ssrn_4673217 crossref_primary_10_1007_s10664_024_10521_0 crossref_primary_10_1007_s00246_020_02518_5 crossref_primary_10_1007_s11042_022_14305_w crossref_primary_10_1016_j_jdent_2022_104115 crossref_primary_10_1016_j_acra_2022_03_025 crossref_primary_10_1148_ryai_230019 crossref_primary_10_1016_j_media_2024_103296 crossref_primary_10_1007_s00530_021_00884_5 crossref_primary_10_1007_s11517_023_02867_2 crossref_primary_10_4329_wjr_v15_i12_359 crossref_primary_10_3390_diagnostics11122206 crossref_primary_10_1016_j_compbiomed_2023_107295 crossref_primary_10_3174_ajnr_A8211 crossref_primary_10_1016_j_engappai_2024_109817 crossref_primary_10_1007_s00247_020_04895_8 crossref_primary_10_1038_s41746_021_00544_y crossref_primary_10_1148_ryai_230006 crossref_primary_10_3390_app11219904 crossref_primary_10_1186_s12880_024_01253_0 crossref_primary_10_3390_s23219004 crossref_primary_10_3389_fcvm_2021_768245 crossref_primary_10_3390_ai3030043 crossref_primary_10_1016_j_media_2024_103282 crossref_primary_10_1109_ACCESS_2025_3532719 crossref_primary_10_1186_s12885_024_12456_7 crossref_primary_10_1007_s11548_024_03213_z crossref_primary_10_14366_usg_20078 crossref_primary_10_3389_fradi_2023_1149461 crossref_primary_10_1007_s00330_022_09184_6 crossref_primary_10_1056_NEJMra2301725 crossref_primary_10_1186_s13244_022_01311_7 crossref_primary_10_2214_AJR_22_27487 crossref_primary_10_1007_s00784_025_06216_5 crossref_primary_10_1016_j_rmed_2020_106271 crossref_primary_10_1097_RCT_0000000000001169 crossref_primary_10_1109_TRPMS_2022_3231702 crossref_primary_10_20517_ais_2024_36 crossref_primary_10_47671_TVG_79_23_110 crossref_primary_10_1002_adfm_202305136 crossref_primary_10_1016_j_jcjq_2020_08_002 crossref_primary_10_3390_tomography8030117 crossref_primary_10_2196_49949 crossref_primary_10_1016_j_media_2021_102246 crossref_primary_10_1200_CCI_23_00074 crossref_primary_10_1016_j_artmed_2023_102553 crossref_primary_10_1259_dmfr_20210185 crossref_primary_10_1016_j_ejmp_2021_02_006 crossref_primary_10_1016_j_ejmp_2021_02_007 crossref_primary_10_38079_igusabder_1326830 crossref_primary_10_1186_s40537_021_00419_9 crossref_primary_10_3390_s24144668 crossref_primary_10_1053_j_gastro_2023_08_034 crossref_primary_10_1148_ryai_230227 crossref_primary_10_3390_app11052187 crossref_primary_10_3390_machines11030325 crossref_primary_10_1007_s10278_022_00649_0 crossref_primary_10_1016_j_csi_2025_103973 crossref_primary_10_1038_s41551_024_01257_9 crossref_primary_10_1038_s41551_021_00704_1 crossref_primary_10_1097_RCT_0000000000001183 crossref_primary_10_1067_j_cpradiol_2022_08_001 crossref_primary_10_1016_j_amc_2024_128952 crossref_primary_10_3389_fbioe_2024_1404058 crossref_primary_10_3348_jksr_2020_0177 crossref_primary_10_46904_eea_23_71_4_1108004 crossref_primary_10_1007_s10278_022_00748_y crossref_primary_10_1109_RBME_2024_3496744 crossref_primary_10_1148_ryai_230337 crossref_primary_10_4236_jcc_2021_911010 crossref_primary_10_3233_ADR_240129 crossref_primary_10_3349_ymj_2022_63_S74 crossref_primary_10_3389_fphys_2024_1342572 crossref_primary_10_6009_jjrt_2023_1418 crossref_primary_10_1007_s00330_023_09860_1 crossref_primary_10_1117_1_JMI_10_6_061404 crossref_primary_10_1016_j_ejmp_2021_02_026 crossref_primary_10_1016_j_jvcir_2024_104296 crossref_primary_10_1371_journal_pone_0305947 crossref_primary_10_1016_j_procir_2024_08_254 crossref_primary_10_3389_fdgth_2021_671015 crossref_primary_10_1038_s41585_021_00465_1 crossref_primary_10_1038_s41598_022_11549_2 crossref_primary_10_1007_s43545_024_00954_1 crossref_primary_10_1016_j_knosys_2022_108230 crossref_primary_10_1038_s41597_020_00674_0 crossref_primary_10_1109_ACCESS_2023_3292158 crossref_primary_10_3390_technologies10060119 crossref_primary_10_1007_s10278_022_00594_y crossref_primary_10_1142_S2810958923300056 crossref_primary_10_1016_j_ejrad_2020_109083 crossref_primary_10_1038_s41598_022_19045_3 crossref_primary_10_1007_s00330_022_08724_4 crossref_primary_10_1007_s10278_022_00605_y crossref_primary_10_1097_RLI_0000000000001106 crossref_primary_10_3390_cancers14133063 crossref_primary_10_1007_s11042_022_12932_x crossref_primary_10_1097_RLI_0000000000001126 crossref_primary_10_2196_60521 crossref_primary_10_3390_diagnostics12071601 crossref_primary_10_1016_j_xops_2024_100623 crossref_primary_10_1016_j_jacep_2025_03_002 crossref_primary_10_1212_WNL_0000000000206808 crossref_primary_10_1016_j_compbiomed_2023_106901 crossref_primary_10_1111_isj_12567 crossref_primary_10_1148_ryai_230550 crossref_primary_10_3348_kjr_2021_0048 crossref_primary_10_1016_j_clinimag_2021_02_017 crossref_primary_10_1007_s00259_020_05167_1 crossref_primary_10_4274_dir_2024_242830 crossref_primary_10_1016_j_jvir_2023_07_019 crossref_primary_10_1007_s00256_021_03824_6 crossref_primary_10_1016_j_cviu_2024_104269 crossref_primary_10_1016_j_xops_2024_100477 crossref_primary_10_1259_bjr_20211028 crossref_primary_10_1007_s12553_021_00586_y crossref_primary_10_1007_s10278_024_01083_0 crossref_primary_10_1259_dmfr_20200611 crossref_primary_10_35596_1729_7648_2025_23_1_60_67 crossref_primary_10_3389_fradi_2021_748968 crossref_primary_10_3390_s24051664 crossref_primary_10_1007_s00414_023_02958_7 crossref_primary_10_1016_j_media_2024_103107 crossref_primary_10_1038_s41598_022_22222_z crossref_primary_10_4250_jcvi_2021_0039 crossref_primary_10_1038_s41551_023_01049_7 crossref_primary_10_1148_ryai_220055 crossref_primary_10_1016_j_artmed_2024_102861 crossref_primary_10_1148_ryai_220299 crossref_primary_10_1186_s13244_020_00929_9 crossref_primary_10_1080_21681163_2022_2156391 crossref_primary_10_3390_bioengineering11040338 crossref_primary_10_1016_j_patter_2021_100426 crossref_primary_10_24835_1607_0763_1346 crossref_primary_10_1007_s11042_024_20292_x crossref_primary_10_1148_ryai_210026 crossref_primary_10_1016_j_matpr_2021_12_016 crossref_primary_10_1016_j_media_2024_103335 crossref_primary_10_1007_s00330_021_07782_4 crossref_primary_10_1007_s10278_024_01095_w crossref_primary_10_1371_journal_pdig_0000102 crossref_primary_10_1088_1361_6560_ac6d9c crossref_primary_10_1016_j_dche_2022_100072 crossref_primary_10_4103_ijo_IJO_424_22 crossref_primary_10_1111_ajt_16934 crossref_primary_10_3389_fonc_2023_1290313 crossref_primary_10_1208_s12249_024_02973_w crossref_primary_10_12968_hmed_2024_0312 crossref_primary_10_1007_s11604_022_01249_2 crossref_primary_10_3390_bioengineering11050484 crossref_primary_10_1038_s41598_023_37508_z crossref_primary_10_1148_ryai_240377 crossref_primary_10_1148_ryai_240376 crossref_primary_10_1038_s41467_023_41820_7 crossref_primary_10_1186_s12911_022_01843_4 crossref_primary_10_3390_cancers15041174 crossref_primary_10_1007_s43681_023_00329_x crossref_primary_10_1016_j_compbiomed_2024_109261 crossref_primary_10_3348_kjr_2022_0588 crossref_primary_10_3390_diagnostics12123112 crossref_primary_10_1007_s00417_024_06420_1 crossref_primary_10_32604_cmc_2021_013159 crossref_primary_10_1016_j_radphyschem_2021_109437 crossref_primary_10_3390_diagnostics13101799 crossref_primary_10_1148_ryai_220270 crossref_primary_10_1016_j_ajur_2022_05_003 crossref_primary_10_1038_s41598_021_87762_2 crossref_primary_10_1183_16000617_0055_2023 crossref_primary_10_1007_s00330_021_07781_5 crossref_primary_10_1148_rg_2021200210 crossref_primary_10_1148_radiol_212151 crossref_primary_10_17816_DD60635 crossref_primary_10_1002_ail2_100 crossref_primary_10_3390_cancers17020285 crossref_primary_10_3390_su15032737 crossref_primary_10_1177_15330338221126869 crossref_primary_10_3389_fmed_2024_1360143 crossref_primary_10_1016_j_cpet_2021_09_010 crossref_primary_10_1177_08465371231180844 crossref_primary_10_1007_s00586_022_07155_5 crossref_primary_10_1016_j_ejrad_2024_111399 crossref_primary_10_1167_tvst_10_8_2 crossref_primary_10_3390_cancers14163867 crossref_primary_10_1016_j_compbiomed_2022_105464 crossref_primary_10_1109_TRPMS_2024_3436697 crossref_primary_10_3390_diagnostics11112049 crossref_primary_10_3389_fnins_2022_832276 crossref_primary_10_3390_diagnostics13061070 crossref_primary_10_1088_1361_6560_ad1f86 crossref_primary_10_3233_IDT_230231 crossref_primary_10_1007_s13246_024_01408_x crossref_primary_10_1016_j_procs_2024_05_081 crossref_primary_10_1039_D2AN02057B crossref_primary_10_1148_radiol_223170 crossref_primary_10_1038_s41597_024_03836_6 crossref_primary_10_1007_s10278_020_00413_2 crossref_primary_10_1158_1078_0432_CCR_22_1663 crossref_primary_10_1161_CIR_0000000000001202 crossref_primary_10_3390_cancers16152761 crossref_primary_10_1016_j_media_2025_103553 crossref_primary_10_1016_j_compbiomed_2022_105458 crossref_primary_10_1021_acs_analchem_3c02688 crossref_primary_10_1016_j_compbiomed_2023_106962 crossref_primary_10_1007_s12194_024_00827_5 crossref_primary_10_1186_s12903_022_02539_x crossref_primary_10_1016_j_media_2024_103258 crossref_primary_10_1186_s12859_022_04979_2 crossref_primary_10_1007_s00261_020_02741_x crossref_primary_10_1016_j_ins_2022_05_076 crossref_primary_10_3390_app10238575 crossref_primary_10_1038_s41591_021_01614_0 crossref_primary_10_3390_biomedinformatics4020050 crossref_primary_10_1002_btm2_70002 crossref_primary_10_1016_j_artmed_2024_102814 crossref_primary_10_3389_fradi_2022_881777 crossref_primary_10_1093_radadv_umae035 crossref_primary_10_1007_s10278_022_00717_5 crossref_primary_10_1016_j_neuroimage_2021_118606 crossref_primary_10_1038_s41416_021_01333_w crossref_primary_10_1371_journal_pone_0315434 crossref_primary_10_1016_j_compbiomed_2023_107823 crossref_primary_10_1615_CritRevOncog_2023050852 crossref_primary_10_1038_s41698_024_00789_2 crossref_primary_10_1148_ryai_220007 crossref_primary_10_1016_j_matpr_2021_04_241 crossref_primary_10_1109_TMI_2023_3260974 crossref_primary_10_3389_frai_2025_1512438 crossref_primary_10_3390_ai5040111 crossref_primary_10_1016_j_ejmp_2021_04_004 crossref_primary_10_1002_mp_15551 crossref_primary_10_3389_fdgth_2024_1456911 crossref_primary_10_3389_fncom_2021_785244 crossref_primary_10_1038_s41598_022_16798_9 crossref_primary_10_1016_j_jacr_2022_05_022 crossref_primary_10_1117_1_JMI_11_1_014501 crossref_primary_10_1016_j_jvsvi_2023_100016 crossref_primary_10_1016_j_media_2023_103059 crossref_primary_10_1080_17474086_2023_2192474 crossref_primary_10_18705_2782_3806_2024_4_5_384_389 crossref_primary_10_1145_3592616 crossref_primary_10_3390_rs14236031 crossref_primary_10_3390_s24154777 crossref_primary_10_1038_s42256_023_00625_5 crossref_primary_10_1097_RTI_0000000000000705 crossref_primary_10_1111_vru_13062 crossref_primary_10_3389_fnimg_2022_1012639 crossref_primary_10_1016_j_bspc_2023_105081 crossref_primary_10_2147_RMI_S295205 crossref_primary_10_3390_diagnostics11050742 crossref_primary_10_32604_cmc_2023_040264 crossref_primary_10_7759_cureus_46454 crossref_primary_10_1302_0301_620X_104B8_BJJ_2022_0416 crossref_primary_10_1038_s41571_022_00707_0 crossref_primary_10_1016_j_cpet_2021_06_003 crossref_primary_10_1038_s41598_024_74229_3 crossref_primary_10_1007_s11912_021_01054_6 crossref_primary_10_1016_j_cpet_2021_06_001 crossref_primary_10_1007_s00259_023_06422_x crossref_primary_10_3389_fradi_2024_1283392 crossref_primary_10_21105_joss_02868 crossref_primary_10_1016_j_inffus_2023_102060 crossref_primary_10_3390_biomedicines12081753 crossref_primary_10_1080_21655979_2023_2244232 crossref_primary_10_1117_1_NPh_10_3_035004 crossref_primary_10_1183_23120541_00145_2023 crossref_primary_10_1016_j_eswa_2023_121165 crossref_primary_10_1177_08465371221145023 crossref_primary_10_1109_ACCESS_2022_3178382 crossref_primary_10_1089_neu_2022_0201 crossref_primary_10_1007_s11042_022_13991_w crossref_primary_10_1016_j_bj_2024_100743 crossref_primary_10_1186_s41747_020_00159_0 crossref_primary_10_1371_journal_pone_0267976 crossref_primary_10_3390_jimaging8100270 crossref_primary_10_1007_s12574_023_00611_1 crossref_primary_10_1038_s41598_021_86995_5 crossref_primary_10_1080_17452759_2024_2424463 crossref_primary_10_1109_JBHI_2022_3152267 crossref_primary_10_1007_s11517_024_03066_3 crossref_primary_10_1016_j_radphyschem_2022_110300 crossref_primary_10_1016_j_acra_2020_12_026 crossref_primary_10_1148_radiol_221257 crossref_primary_10_1007_s00247_023_05746_y crossref_primary_10_1126_science_adj3312 crossref_primary_10_1111_jon_13078 crossref_primary_10_1109_ACCESS_2022_3154061 crossref_primary_10_35596_1729_7648_2023_21_1_66_73 crossref_primary_10_1029_2023JD040126 crossref_primary_10_1016_j_jpi_2023_100357 crossref_primary_10_1016_j_rmed_2024_107809 crossref_primary_10_1620_tjem_2024_J071 crossref_primary_10_3389_frai_2023_1253183 crossref_primary_10_3389_fradi_2023_1202412 crossref_primary_10_1016_j_mri_2023_07_001 crossref_primary_10_1002_mrm_29750 crossref_primary_10_1007_s00330_020_07148_2 crossref_primary_10_1016_j_gpb_2023_09_003 crossref_primary_10_1038_s41598_024_62151_7 crossref_primary_10_1021_acssensors_1c01618 crossref_primary_10_1016_j_patter_2022_100641 crossref_primary_10_1038_s41598_024_58223_3 crossref_primary_10_1038_s41597_023_02457_9 crossref_primary_10_1109_JBHI_2021_3110805 crossref_primary_10_1148_ryai_210290 crossref_primary_10_1140_epjs_s11734_024_01289_x crossref_primary_10_1007_s10278_021_00450_5 crossref_primary_10_3390_environments10120204 crossref_primary_10_1016_j_bspc_2025_107734 crossref_primary_10_1148_radiol_222126 crossref_primary_10_1002_hcs2_40 crossref_primary_10_1177_20552076211048654 crossref_primary_10_1016_j_biocel_2022_106195 crossref_primary_10_1016_j_semradonc_2022_06_009 crossref_primary_10_1007_s00234_021_02890_w crossref_primary_10_3389_fcvm_2023_1213290 crossref_primary_10_3389_fonc_2022_960984 crossref_primary_10_1109_MTS_2021_3056173 crossref_primary_10_1016_j_ejmp_2022_08_004 crossref_primary_10_1007_s00247_022_05368_w crossref_primary_10_1055_s_0044_1790232 crossref_primary_10_1016_j_compbiomed_2023_106576 crossref_primary_10_1007_s00330_024_10907_0 crossref_primary_10_1155_2022_9699612 crossref_primary_10_1117_1_JMI_9_3_035504 crossref_primary_10_1016_j_jid_2023_02_035 crossref_primary_10_1007_s10589_022_00392_w crossref_primary_10_3390_app10082908 crossref_primary_10_1016_j_matdes_2022_110735 crossref_primary_10_1007_s00234_023_03126_9 crossref_primary_10_1016_j_neucom_2021_01_065 crossref_primary_10_1088_2057_1976_ac37ab crossref_primary_10_1186_s12877_023_04477_x crossref_primary_10_1002_jmri_29748 crossref_primary_10_1136_flgastro_2021_101994 crossref_primary_10_1016_j_imu_2022_101017 crossref_primary_10_1148_ryai_210285 crossref_primary_10_1186_s12903_024_03970_y crossref_primary_10_1016_j_rx_2022_03_003 crossref_primary_10_1148_ryai_210284 crossref_primary_10_1186_s40644_021_00413_7 crossref_primary_10_1148_ryai_240225 crossref_primary_10_1148_ryai_240345 crossref_primary_10_1145_3586580 crossref_primary_10_1016_j_eswa_2024_123660 crossref_primary_10_7717_peerj_cs_2371 crossref_primary_10_29194_NJES_27040465 crossref_primary_10_1016_j_nec_2022_02_012 crossref_primary_10_1016_j_fsisyn_2023_100319 crossref_primary_10_1038_s43856_021_00008_0 crossref_primary_10_1186_s12880_022_00766_w crossref_primary_10_3389_fimag_2023_1175860 crossref_primary_10_3389_frai_2025_1533508 crossref_primary_10_1007_s10140_024_02306_1 crossref_primary_10_1097_RCT_0000000000001316 crossref_primary_10_1148_ryai_2021210014 crossref_primary_10_1109_TVCG_2021_3114784 crossref_primary_10_1007_s13721_024_00491_0 crossref_primary_10_1016_j_compbiomed_2024_108709 crossref_primary_10_1016_j_jvcir_2023_103949 crossref_primary_10_32604_cmes_2025_059152 crossref_primary_10_1016_j_jhepr_2022_100443 crossref_primary_10_1088_1361_6560_ac1157 crossref_primary_10_1155_2022_8026580 crossref_primary_10_3390_biomedicines10112796 crossref_primary_10_1016_j_engappai_2022_104978 crossref_primary_10_1007_s10462_022_10256_8 crossref_primary_10_1016_j_radonc_2024_110179 crossref_primary_10_1088_1741_2552_aceca3 crossref_primary_10_1007_s00784_023_04992_6 crossref_primary_10_1016_j_mrl_2024_200138 crossref_primary_10_1088_1361_6560_ad8857 crossref_primary_10_1016_j_ejrad_2021_109808 crossref_primary_10_3233_THC_230920 crossref_primary_10_1055_a_1717_0955 crossref_primary_10_3389_fonc_2022_742701 crossref_primary_10_1016_j_jacr_2021_09_022 crossref_primary_10_3389_fcimb_2024_1384809 crossref_primary_10_1007_s10278_022_00690_z crossref_primary_10_3390_bioengineering12010009 crossref_primary_10_15701_kcgs_2023_29_5_21 crossref_primary_10_1364_BOE_453536 crossref_primary_10_1093_eurheartj_ehad456 crossref_primary_10_3389_frai_2022_813842 crossref_primary_10_1186_s12968_021_00824_2 crossref_primary_10_3174_ajnr_A7850 crossref_primary_10_3389_fradi_2023_1242902 crossref_primary_10_3390_app14177822 crossref_primary_10_1111_jgh_15415 crossref_primary_10_1146_annurev_bioeng_083120_023315 crossref_primary_10_1016_j_compmedimag_2021_102013 crossref_primary_10_59717_j_xinn_life_2024_100079 crossref_primary_10_1016_j_jdent_2025_105690 crossref_primary_10_1016_j_media_2022_102396 crossref_primary_10_2196_38655 crossref_primary_10_1007_s00428_023_03589_4 crossref_primary_10_32604_cmc_2022_028154 crossref_primary_10_1016_j_cjca_2024_04_026 crossref_primary_10_3390_onco1020016 crossref_primary_10_1016_j_jacr_2022_08_006 crossref_primary_10_1186_s13244_022_01220_9 crossref_primary_10_1016_j_cmpb_2024_108117 crossref_primary_10_1111_cas_16395 crossref_primary_10_3174_ajnr_A7500 crossref_primary_10_3390_app12063190 crossref_primary_10_1007_s00330_022_09056_z crossref_primary_10_1097_ICU_0000000000000785 crossref_primary_10_1016_j_metrad_2024_100098 crossref_primary_10_1016_S2589_7500_21_00056_X crossref_primary_10_1007_s10439_023_03232_y crossref_primary_10_1002_hbm_26683 crossref_primary_10_1259_bjr_20220215 crossref_primary_10_1093_bjr_tqae108 crossref_primary_10_1007_s00247_022_05483_8 crossref_primary_10_1093_bjrai_ubae006 crossref_primary_10_1016_j_patcog_2021_108139 crossref_primary_10_1016_j_neucom_2022_07_005 crossref_primary_10_2196_50342 crossref_primary_10_1109_ACCESS_2022_3181730 crossref_primary_10_1038_s41598_025_90453_x crossref_primary_10_1021_acs_chemrev_3c00070 crossref_primary_10_1007_s44206_022_00009_z crossref_primary_10_1016_j_heliyon_2023_e17104 crossref_primary_10_1007_s11227_023_05448_0 crossref_primary_10_1016_j_ssci_2023_106180 crossref_primary_10_1016_j_media_2023_102868 crossref_primary_10_1007_s42979_024_03025_7 crossref_primary_10_2139_ssrn_4017219 crossref_primary_10_1007_s42979_023_01882_2 crossref_primary_10_1016_j_ejrad_2021_110073 crossref_primary_10_1016_j_ejrad_2022_110291 crossref_primary_10_1038_s43856_024_00538_3 crossref_primary_10_1016_j_rxeng_2022_03_005 crossref_primary_10_1016_j_tele_2023_102085 crossref_primary_10_29194_NJES_27040396 crossref_primary_10_3390_diagnostics13050974 crossref_primary_10_1038_s41597_023_02125_y crossref_primary_10_1088_1361_6560_ac678a crossref_primary_10_1016_j_placenta_2023_02_009 crossref_primary_10_1007_s10278_021_00505_7 crossref_primary_10_1155_2021_7259414 crossref_primary_10_1097_RCT_0000000000001374 crossref_primary_10_1111_jdv_18963 crossref_primary_10_1177_26317745241306562 crossref_primary_10_26599_AIR_2024_9150035 crossref_primary_10_60084_ijcr_v2i1_150 crossref_primary_10_1002_ima_22674 crossref_primary_10_1016_j_ejrad_2024_111852 crossref_primary_10_1093_hropen_hoad031 crossref_primary_10_1148_radiol_232471 crossref_primary_10_1007_s00146_023_01684_3 crossref_primary_10_4103_RID_RID_39_22 crossref_primary_10_3390_inventions8060153 crossref_primary_10_1007_s00261_024_04379_5 crossref_primary_10_1016_j_compbiomed_2023_107023 crossref_primary_10_1007_s00330_020_07056_5 crossref_primary_10_1016_j_acra_2021_08_002 crossref_primary_10_1097_RLI_0000000000000673 crossref_primary_10_1186_s13244_024_01833_2 crossref_primary_10_1038_s41586_023_06555_x crossref_primary_10_1115_1_4062808 crossref_primary_10_1007_s10278_022_00658_z crossref_primary_10_1002_jmri_29100 crossref_primary_10_1016_j_imu_2024_101575 crossref_primary_10_1002_jmri_29101 crossref_primary_10_1002_adma_202419579 crossref_primary_10_1007_s10140_023_02120_1 |
| Cites_doi | 10.1007/s13244-017-0588-8 10.1007/978-3-319-59050-9_12 10.1002/jmri.25970 10.1109/TMI.2004.828354 10.1148/radiol.11110938 10.1148/radiol.12112438 10.1093/jamia/ocy017 10.1109/TASL.2007.911503 10.1016/j.neuroimage.2012.10.064 10.1148/radiol.2019182908 10.1148/radiol.2017170236 10.1038/mp.2013.78 10.1148/radiol.2272012051 10.1109/TMI.2019.2899534 10.1109/TMI.2018.2881415 10.1038/s41591-018-0272-7 10.1148/radiol.2017162664 10.1016/j.neuroimage.2013.05.041 10.1148/radiol.2018180694 10.1016/j.jalz.2005.06.003 10.1007/978-3-319-94878-2_14 10.1148/radiol.2018181422 10.1007/s10278-013-9622-7 10.1148/radiol.2018180513 10.1056/NEJMoa0800996 10.1148/radiol.2018180958 10.1038/sdata.2017.117 10.1197/jamia.M2716 10.1109/TMI.2018.2827462 10.1148/radiol.2019191225 10.1007/s00330-015-3794-0 10.1148/radiol.2019190613 10.1038/sdata.2017.177 10.1001/jamanetworkopen.2018.5474 10.1038/s41746-017-0015-z 10.1007/978-3-319-94878-2_6 10.1002/jmri.21049 10.1093/jamia/ocw028 10.1371/journal.pmed.1002686 10.1118/1.3528204 10.1371/journal.pmed.1002699 10.1001/jama.2016.17216 10.1158/0008-5472.CAN-17-0334 10.1016/S2589-7500(19)30123-2 10.1016/j.artmed.2018.11.004 10.1016/j.media.2016.05.009 10.4137/CIN.S37032 10.1016/j.jclinepi.2008.04.008 10.1007/s10278-011-9426-6 10.1109/TMI.2014.2303821 10.1016/j.acra.2019.09.014 10.1148/radiol.10091808 10.1109/TMI.2014.2377694 10.2214/AJR.12.10136 10.1148/rg.2017170047 10.1001/jamanetworkopen.2019.1095 10.1109/MeMeA.2018.8438639 10.1145/1401890.1401965 10.1016/j.neuroimage.2015.05.049 10.1148/ryai.2019180066 10.1007/s00330-018-5810-7 10.1613/jair.606 10.1148/radiol.2017171920 10.1109/MIS.2009.36 10.1007/s10278-009-9193-9 10.1148/rg.2015140244 10.18383/j.tom.2018.00055 10.2214/AJR.07.3354 10.1148/radiol.2017171115 10.1148/radiol.2019190938 10.1038/s41598-017-10371-5 10.7717/peerj.2057 10.1148/radiol.2018180547 10.1093/jamia/ocw112 10.1148/radiol.2019182304 10.1109/TMI.2017.2708987 10.1136/amiajnl-2012-000896 10.1038/sdata.2016.18 10.1007/s10278-016-9938-1 |
| ContentType | Journal Article |
| Copyright | RSNA, 2020. 2020 by the Radiological Society of North America, Inc. 2020 |
| Copyright_xml | – notice: RSNA, 2020. – notice: 2020 by the Radiological Society of North America, Inc. 2020 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM |
| DOI | 10.1148/radiol.2020192224 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | 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 |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1527-1315 |
| EndPage | 15 |
| ExternalDocumentID | PMC7104701 32068507 10_1148_radiol_2020192224 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NLM NIH HHS grantid: R01 LM012966 – fundername: NCATS NIH HHS grantid: UL1 TR003142 – fundername: NCI NIH HHS grantid: U01 CA190214 – fundername: NCATS NIH HHS grantid: UL1 TR001085 – fundername: NCI NIH HHS grantid: U01 CA242879 – fundername: NCI NIH HHS grantid: U01 CA142555 – fundername: NCI NIH HHS grantid: U01 CA187947 – fundername: ; ; grantid: U01CA142555 – fundername: ; ; grantid: UL1 TR001085 |
| GroupedDBID | --- .55 .GJ 123 18M 1CY 1KJ 29P 2WC 34G 39C 4.4 53G 5RE 6NX 6PF 7FM AAEJM AAQQT AAWTL AAYXX ABDPE ABHFT ABOCM ACFQH ACGFO ACJAN ADBBV AENEX AENYM AFFNX AFOSN AJJEV AJWWR ALMA_UNASSIGNED_HOLDINGS BAWUL CITATION CS3 DIK DU5 E3Z EBS EJD F5P F9R GX1 H13 J5H KO8 L7B LMP LSO MJL MV1 N4W OK1 P2P R.V RKKAF RXW SJN TAE TR2 TRS TWZ W8F WH7 WOQ X7M YQI YQJ ZGI ZVN ZXP ACRZS CGR CUY CVF ECM EIF NPM VXZ ZKG 7X8 5PM |
| ID | FETCH-LOGICAL-c399t-cd5d70e48b0c4ab870f43f2103e0b8492b6bb0356f1cdd841f936cb807145dce3 |
| ISSN | 0033-8419 1527-1315 |
| IngestDate | Thu Aug 21 13:36:04 EDT 2025 Thu Jul 10 21:05:12 EDT 2025 Wed Feb 19 02:05:35 EST 2025 Tue Jul 01 00:43:48 EDT 2025 Thu Apr 24 23:12:28 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | RSNA, 2020. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c399t-cd5d70e48b0c4ab870f43f2103e0b8492b6bb0356f1cdd841f936cb807145dce3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-8949-8858 0000-0002-6991-6557 0000-0003-3564-9056 0000-0001-8081-7376 0000-0002-8591-5861 0000-0003-0715-0952 0000-0003-4528-1312 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/7104701 |
| PMID | 32068507 |
| PQID | 2357446847 |
| PQPubID | 23479 |
| PageCount | 12 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7104701 proquest_miscellaneous_2357446847 pubmed_primary_32068507 crossref_citationtrail_10_1148_radiol_2020192224 crossref_primary_10_1148_radiol_2020192224 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2020-04-01 |
| PublicationDateYYYYMMDD | 2020-04-01 |
| PublicationDate_xml | – month: 04 year: 2020 text: 2020-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Radiology |
| PublicationTitleAlternate | Radiology |
| PublicationYear | 2020 |
| Publisher | Radiological Society of North America |
| Publisher_xml | – name: Radiological Society of North America |
| References | r2 r3 r4 r5 r6 r7 r8 r9 r91 r94 r93 r96 r95 r10 r12 r11 r14 r13 r16 r15 r18 r17 r19 Brady A (r58) 2012; 81 Schlegl T (r68) r21 r20 r22 r25 r24 r29 Nuance (r26) Ray L (r88) r30 r32 r34 r33 r36 r112 r35 r38 r37 r39 Rajpurkar P (r107) McMahan B (r98) r110 r111 Conway D (r43) 2011 r41 r40 r105 r42 r106 r45 r44 r108 r46 r102 r49 r103 r48 r104 Do HM (r62) 2019 r109 Choi E (r55) 2017; 24 r50 r51 r54 r53 r56 Xi P (r78) r57 Three-Palm-Software (r90) r59 Irvin J (r79) Su H (r100) Lipton ZC (r52) r61 U.S. Department of Labor (r101) 2004 r63 r65 r64 r66 Heim E (r47) 2018; 5 Yan K (r60) 2018; 5 r70 Korkinof D (r69) r72 r71 r74 r73 r76 r75 r77 Dean J (r99) 2012; 25 DICOM-Standards-Committee (r92) r81 r80 r83 r82 r84 r1 |
| References_xml | – ident: r40 doi: 10.1007/s13244-017-0588-8 – ident: r96 – volume-title: Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery ident: r68 doi: 10.1007/978-3-319-59050-9_12 – ident: r5 doi: 10.1002/jmri.25970 – ident: r44 doi: 10.1109/TMI.2004.828354 – ident: r45 doi: 10.1148/radiol.11110938 – ident: r51 doi: 10.1148/radiol.12112438 – ident: r35 doi: 10.1093/jamia/ocy017 – ident: r42 doi: 10.1109/TASL.2007.911503 – ident: r75 doi: 10.1016/j.neuroimage.2012.10.064 – ident: r4 doi: 10.1148/radiol.2019182908 – ident: r15 doi: 10.1148/radiol.2017170236 – ident: r73 doi: 10.1038/mp.2013.78 – ident: r66 doi: 10.1148/radiol.2272012051 – ident: r16 doi: 10.1109/TMI.2019.2899534 – ident: r70 doi: 10.1109/TMI.2018.2881415 – ident: r32 doi: 10.1038/s41591-018-0272-7 – volume-title: A Critical Review of Recurrent Neural Networks for Sequence Learning ident: r52 – ident: r3 doi: 10.1148/radiol.2017162664 – ident: r77 doi: 10.1016/j.neuroimage.2013.05.041 – ident: r18 doi: 10.1148/radiol.2018180694 – ident: r53 – ident: r103 doi: 10.1016/j.jalz.2005.06.003 – ident: r34 doi: 10.1007/978-3-319-94878-2_14 – volume-title: Machine Learning for Email: Spam Filtering and Priority Inbox. Sebastopol year: 2011 ident: r43 – ident: r2 doi: 10.1148/radiol.2018181422 – volume-title: Experiments on Parallel Training of Deep Neural Network using Model Averaging ident: r100 – ident: r111 doi: 10.1007/s10278-013-9622-7 – ident: r20 doi: 10.1148/radiol.2018180513 – ident: r102 doi: 10.1056/NEJMoa0800996 – ident: r13 doi: 10.1148/radiol.2018180958 – ident: r72 doi: 10.1038/sdata.2017.117 – ident: r46 – ident: r33 doi: 10.1197/jamia.M2716 – volume-title: mPower Clinical Analytics for medical imaging ident: r26 – ident: r9 doi: 10.1109/TMI.2018.2827462 – volume: 5 start-page: 034002 issue: 3 year: 2018 ident: r47 publication-title: J Med Imaging (Bellingham) – ident: r11 doi: 10.1148/radiol.2019191225 – ident: r29 doi: 10.1007/s00330-015-3794-0 – ident: r41 – ident: r1 doi: 10.1148/radiol.2019190613 – ident: r36 doi: 10.1038/sdata.2017.177 – ident: r112 doi: 10.1001/jamanetworkopen.2018.5474 – ident: r12 doi: 10.1038/s41746-017-0015-z – ident: r24 doi: 10.1007/978-3-319-94878-2_6 – ident: r74 doi: 10.1002/jmri.21049 – ident: r57 doi: 10.1093/jamia/ocw028 – ident: r83 doi: 10.1371/journal.pmed.1002686 – ident: r106 doi: 10.1118/1.3528204 – ident: r80 doi: 10.1371/journal.pmed.1002699 – ident: r82 doi: 10.1001/jama.2016.17216 – ident: r49 doi: 10.1158/0008-5472.CAN-17-0334 – ident: r84 doi: 10.1016/S2589-7500(19)30123-2 – ident: r54 doi: 10.1016/j.artmed.2018.11.004 – ident: r19 doi: 10.1016/j.media.2016.05.009 – ident: r93 doi: 10.4137/CIN.S37032 – ident: r81 doi: 10.1016/j.jclinepi.2008.04.008 – volume-title: DICOM PS3.3 2019e - Information Object Definitions ident: r92 – ident: r50 doi: 10.1007/s10278-011-9426-6 – ident: r109 doi: 10.1109/TMI.2014.2303821 – ident: r61 doi: 10.1016/j.acra.2019.09.014 – volume: 25 start-page: 1 year: 2012 ident: r99 publication-title: Adv Neural Inf Process Syst – ident: r108 doi: 10.1148/radiol.10091808 – ident: r76 doi: 10.1109/TMI.2014.2377694 – ident: r91 doi: 10.2214/AJR.12.10136 – ident: r59 doi: 10.1148/rg.2017170047 – ident: r10 doi: 10.1001/jamanetworkopen.2019.1095 – volume-title: Abnormality Detection in Mammography using Deep Convolutional Neural Networks ident: r78 doi: 10.1109/MeMeA.2018.8438639 – ident: r39 doi: 10.1145/1401890.1401965 – volume-title: MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs ident: r107 – ident: r104 doi: 10.1016/j.neuroimage.2015.05.049 – ident: r14 doi: 10.1148/ryai.2019180066 – ident: r7 doi: 10.1007/s00330-018-5810-7 – ident: r38 doi: 10.1613/jair.606 – volume: 81 start-page: 3 issue: 1 year: 2012 ident: r58 publication-title: Ulster Med J – ident: r22 doi: 10.1148/radiol.2017171920 – ident: r64 doi: 10.1109/MIS.2009.36 – volume-title: Employee Benefits Security Administration. The Health Insurance Portability and Accountability Act (HIPAA) year: 2004 ident: r101 – ident: r95 doi: 10.1007/s10278-009-9193-9 – volume-title: Federated Learning: Collaborative Machine Learning without Centralized Training Data. Google AI Blog ident: r98 – volume-title: Detailed and Precise Measurement with Lesion Management ident: r88 – ident: r65 – ident: r110 – ident: r30 doi: 10.1148/rg.2015140244 – volume-title: High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks ident: r69 – ident: r48 doi: 10.18383/j.tom.2018.00055 – ident: r63 doi: 10.2214/AJR.07.3354 – ident: r56 doi: 10.1148/radiol.2017171115 – ident: r17 doi: 10.1148/radiol.2019190938 – volume: 5 start-page: 036501 issue: 3 year: 2018 ident: r60 publication-title: J Med Imaging (Bellingham) – ident: r105 doi: 10.1038/s41598-017-10371-5 – ident: r94 doi: 10.7717/peerj.2057 – volume-title: CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison ident: r79 – ident: r21 doi: 10.1148/radiol.2018180547 – volume: 24 start-page: 361 issue: 2 year: 2017 ident: r55 publication-title: J Am Med Inform Assoc doi: 10.1093/jamia/ocw112 – volume-title: AI Radiomics in a Monogenic Autoimmune Disease: Deep Learning of Routine Radiologist Annotations Correlated with Pathologically Verified Lung Findings year: 2019 ident: r62 – ident: r6 doi: 10.1148/radiol.2019182304 – ident: r8 doi: 10.1109/TMI.2017.2708987 – ident: r37 doi: 10.1136/amiajnl-2012-000896 – ident: r71 doi: 10.1038/sdata.2016.18 – volume-title: LesionOne – Lesion Tracking Application Framework ident: r90 – ident: r25 doi: 10.1007/s10278-016-9938-1 |
| SSID | ssj0014587 |
| Score | 2.71796 |
| SecondaryResourceType | review_article |
| Snippet | Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the... |
| SourceID | pubmedcentral proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 4 |
| SubjectTerms | Algorithms Data Collection Data Management Diagnostic Imaging Humans Machine Learning Reviews and |
| Title | Preparing Medical Imaging Data for Machine Learning |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/32068507 https://www.proquest.com/docview/2357446847 https://pubmed.ncbi.nlm.nih.gov/PMC7104701 |
| Volume | 295 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELagSIgL4s3yUpA4UWVJYsdxjlULaosWIdSqvUV-0qA2i7bZS38949hxki4gyiWK8hjZni_jmcwLoXeppDxlBswSnauYSLBZhVAylpJSpQkjitl858UXun9MDk_z0yGFoMsuacVcXv02r-R_uArXgK82S_YGnA1E4QKcA3_hCByG4z_x-OtK2yaCYOz3_paDC9d1aI-3vIsgXHTBkrqvo_p9rIx-46qe_lW3iYEXYJ36LJ62brYP50EmLy-vdHfrZPkDRII8296ZDxJspbwLY5fX53XA3Mm6A0qtx_8XsmQUluJlJsYxI16waS8msyJOsUvE7OVo5rplTgDjpCIZba_unU3BTWwywqqb99wOAzTPLCPDLtV75q9tXiGk0CVYs8qRqAYSt9GdDEwI291i7-Bz8DCRnLl6qn523uMNJD5sjGKqs2wYItfjaUcKytEDdN9bFtGOg8lDdEs3j9DdhY-deIxwQEvk0RJ5tEQWLRGgJfJoiXq0PEHHnz4e7e7HvmVGLEHTbGOpclUk8I2JRBIuQBgbgg2Y9VgngpEyE1SIBOfUpFIpmLcpMZWC2TS2HMaPn6KtZtno5yiCnVCV1iLglBFJTWmApCo1N0ZokqYzlPSLUklfT962NTmv_siKGXofXvnpiqn87eG3_UpXIPKsH4s3erm-rGyFJkJgVMUMPXMrH8jhLKEMbJwZKiY8CQ_YcurTO0191pVVL2zVkiR9cZNBvkT3hm_mFdpqV2v9GrTUVrzp4PYL93GQKQ |
| linkProvider | Flying Publisher |
| 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=Preparing+Medical+Imaging+Data+for+Machine+Learning&rft.jtitle=Radiology&rft.au=Willemink%2C+Martin+J.&rft.au=Koszek%2C+Wojciech+A.&rft.au=Hardell%2C+Cailin&rft.au=Wu%2C+Jie&rft.date=2020-04-01&rft.issn=0033-8419&rft.eissn=1527-1315&rft.volume=295&rft.issue=1&rft.spage=4&rft.epage=15&rft_id=info:doi/10.1148%2Fradiol.2020192224&rft.externalDBID=n%2Fa&rft.externalDocID=10_1148_radiol_2020192224 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0033-8419&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0033-8419&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0033-8419&client=summon |