A Comparative Study of Deep Neural Networks for Real-Time Semantic Segmentation during the Transurethral Resection of Bladder Tumors
Bladder cancer is a common and often fatal disease. Papillary bladder tumors are well detectable using cystoscopic imaging, but small or flat lesions are frequently overlooked by urologists. However, detection accuracy can be improved if the images from the cystoscope are segmented in real time by a...
Saved in:
Published in | Diagnostics (Basel) Vol. 12; no. 11; p. 2849 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI AG
01.11.2022
MDPI |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Bladder cancer is a common and often fatal disease. Papillary bladder tumors are well detectable using cystoscopic imaging, but small or flat lesions are frequently overlooked by urologists. However, detection accuracy can be improved if the images from the cystoscope are segmented in real time by a deep neural network (DNN). In this paper, we compare eight state-of-the-art DNNs for the semantic segmentation of white-light cystoscopy images: U-Net, UNet++, MA-Net, LinkNet, FPN, PAN, DeepLabv3, and DeepLabv3+. The evaluation includes per-image classification accuracy, per-pixel localization accuracy, prediction speed, and model size. Results show that the best F-score for bladder cancer (91%), the best segmentation map precision (92.91%), and the lowest size (7.93 MB) are also achieved by the PAN model, while the highest speed (6.73 ms) is obtained by DeepLabv3+. These results indicate better tumor localization accuracy than reported in previous studies. It can be concluded that deep neural networks may be extremely useful in the real-time diagnosis and therapy of bladder cancer, and among the eight investigated models, PAN shows the most promising results. |
---|---|
AbstractList | Bladder cancer is a common and often fatal disease. Papillary bladder tumors are well detectable using cystoscopic imaging, but small or flat lesions are frequently overlooked by urologists. However, detection accuracy can be improved if the images from the cystoscope are segmented in real time by a deep neural network (DNN). In this paper, we compare eight state-of-the-art DNNs for the semantic segmentation of white-light cystoscopy images: U-Net, UNet++, MA-Net, LinkNet, FPN, PAN, DeepLabv3, and DeepLabv3+. The evaluation includes per-image classification accuracy, per-pixel localization accuracy, prediction speed, and model size. Results show that the best F-score for bladder cancer (91%), the best segmentation map precision (92.91%), and the lowest size (7.93 MB) are also achieved by the PAN model, while the highest speed (6.73 ms) is obtained by DeepLabv3+. These results indicate better tumor localization accuracy than reported in previous studies. It can be concluded that deep neural networks may be extremely useful in the real-time diagnosis and therapy of bladder cancer, and among the eight investigated models, PAN shows the most promising results. |
Audience | Academic |
Author | Varnyú, Dóra Szirmay-Kalos, László |
AuthorAffiliation | Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, 1111 Budapest, Hungary |
AuthorAffiliation_xml | – name: Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, 1111 Budapest, Hungary |
Author_xml | – sequence: 1 givenname: Dóra orcidid: 0000-0002-9220-5868 surname: Varnyú fullname: Varnyú, Dóra organization: Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, 1111 Budapest, Hungary – sequence: 2 givenname: László orcidid: 0000-0002-8523-2315 surname: Szirmay-Kalos fullname: Szirmay-Kalos, László organization: Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, 1111 Budapest, Hungary |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36428909$$D View this record in MEDLINE/PubMed |
BookMark | eNptkk1vEzEQhleoiJbSX4CEVuLCJcXfXl-QSviqVIFUwtny2rOJw66d2rtFvfPDcZpSGlT74NH4ncd-R_O8OggxQFW9xOiUUoXeOm-WIebR24wJxqRh6kl1RJDkM8Zwc_AgPqxOcl6jshSmDeHPqkMqGGkUUkfV77N6HoeNSWb011B_Hyd3U8eu_gCwqb_ClExfjvFXTD9z3cVUX4LpZws_FC0MJpQPlGA5QBgLIYbaTcmHZT2uoF4kE_KUYFxtKZeQwd5KCv59b5yDVC-mIab8onramT7Dyd15XP349HEx_zK7-Pb5fH52MbNciHFmZQui4bZVAnHBsWLWMdY1ikhsFROioUIoRBBHqGkZatuGgBSGE0Ctsy09rs53XBfNWm-SH0y60dF4fZuIaalNKo560I5a6bgCKolh4FDbdZJi3ipFKcIgC-vdjrWZ2gGcLQ0oLveg-zfBr_QyXmslGoURKYA3d4AUrybIox58ttD3JkCcsiaSFSOMIFykr_-TruOUQmnVVtUQqZhE_1RLUwz40MXyrt1C9ZlkfNsUxIvq9BFV2Q4Gb8uQdb7k9wrorsCmmHOC7t4jRno7i_qRWSxVrx62577m7-TRP-YX3kE |
CitedBy_id | crossref_primary_10_1007_s00500_023_08614_x crossref_primary_10_3390_math12091281 crossref_primary_10_5213_inj_2346286_143 crossref_primary_10_1038_s41585_024_00904_9 crossref_primary_10_1186_s42492_024_00157_8 |
Cites_doi | 10.1038/s41598-022-22797-7 10.2307/1932409 10.1016/j.eururo.2016.06.010 10.1109/TPAMI.2012.213 10.1109/WACV.2018.00163 10.1109/ICCV.2017.324 10.1109/TIP.2021.3106812 10.1109/5.726791 10.1007/978-3-030-01234-2_49 10.1109/CVPR.2017.106 10.1109/CVPR.2017.195 10.1016/j.eururo.2019.08.032 10.1109/ACCESS.2020.3025372 10.1038/s41598-021-91081-x 10.1007/s11548-019-02115-9 10.1155/2022/9580991 10.1007/s10278-019-00227-x 10.1007/978-3-319-24574-4_28 10.1007/978-981-15-5199-4_6 10.1002/rcs.2194 10.1109/VCIP.2017.8305148 10.1109/CVPR.2015.7298594 10.1109/CVPR.2018.00474 10.1200/CCI.17.00126 10.1109/CVPR.2009.5206848 10.1016/j.artmed.2021.102078 10.1007/978-3-030-00889-5_1 10.2139/ssrn.4137336 10.1109/ICCV.2019.00140 10.1109/CVPR.2016.90 10.1109/83.826787 10.1109/CVPR.2016.308 10.1007/s00345-019-03059-0 10.1089/end.2019.0509 |
ContentType | Journal Article |
Copyright | COPYRIGHT 2022 MDPI AG 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
Copyright_xml | – notice: COPYRIGHT 2022 MDPI AG – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
DBID | NPM AAYXX CITATION 3V. 7XB 8FK 8G5 ABUWG AFKRA AZQEC BENPR CCPQU DWQXO GNUQQ GUQSH M2O MBDVC PIMPY PQEST PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
DOI | 10.3390/diagnostics12112849 |
DatabaseName | PubMed CrossRef ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni Edition) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central ProQuest One Community College ProQuest Central ProQuest Central Student Research Library Prep ProQuest research library Research Library (Corporate) Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals - May need to register for free articles |
DatabaseTitle | PubMed CrossRef Publicly Available Content Database Research Library Prep ProQuest Central Student ProQuest Central Basic ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College Research Library (Alumni Edition) ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Research Library ProQuest One Academic ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | Publicly Available Content Database PubMed CrossRef |
Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 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: 3 dbid: BENPR name: AUTh Library subscriptions: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 2075-4418 |
ExternalDocumentID | oai_doaj_org_article_d3c7d59e372a4ed0bff7315b993301e7 A745500805 10_3390_diagnostics12112849 36428909 |
Genre | Journal Article |
GeographicLocations | Hungary |
GeographicLocations_xml | – name: Hungary |
GrantInformation_xml | – fundername: National Research, Development and Innovation Office, Hungary grantid: K-124124 – fundername: New National Excellence Program of the Ministry for Culture; Innovation from the source of the National Research; Development and Innovation Fund; Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program grantid: K–124124; ÚNKP-22-3-I-BME-247 |
GroupedDBID | 3V. 53G 5VS 8G5 AADQD AAFWJ ABDBF ABUWG ADBBV AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BCNDV BENPR BPHCQ CCPQU DWQXO EBD ESX GNUQQ GROUPED_DOAJ GUQSH HYE IAO IHR ITC KQ8 M2O M48 MODMG M~E NPM OK1 PGMZT PIMPY PQQKQ PROAC RIG RPM AAYXX AFPKN CITATION 7XB 8FK MBDVC PQEST PQUKI PRINS Q9U 7X8 5PM |
ID | FETCH-LOGICAL-c566t-c7be685cb960565194cd44f89271c9466836690205008b40bb82e76a52e0bdcb3 |
IEDL.DBID | RPM |
ISSN | 2075-4418 |
IngestDate | Fri Oct 04 13:14:09 EDT 2024 Tue Sep 17 21:31:53 EDT 2024 Sat Oct 05 04:20:15 EDT 2024 Thu Oct 10 16:29:20 EDT 2024 Fri Feb 23 00:20:37 EST 2024 Fri Feb 02 04:39:07 EST 2024 Mon Sep 16 17:25:19 EDT 2024 Wed Oct 16 00:40:16 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 11 |
Keywords | transfer learning bladder cancer convolutional neural network unsharp masking white-light cystoscopy semantic segmentation guided filtering |
Language | English |
License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c566t-c7be685cb960565194cd44f89271c9466836690205008b40bb82e76a52e0bdcb3 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0000-0002-9220-5868 0000-0002-8523-2315 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689102/ |
PMID | 36428909 |
PQID | 2748279470 |
PQPubID | 2032410 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_d3c7d59e372a4ed0bff7315b993301e7 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9689102 proquest_miscellaneous_2740504201 proquest_journals_2748279470 gale_infotracmisc_A745500805 gale_infotracacademiconefile_A745500805 crossref_primary_10_3390_diagnostics12112849 pubmed_primary_36428909 |
PublicationCentury | 2000 |
PublicationDate | 2022-11-01 |
PublicationDateYYYYMMDD | 2022-11-01 |
PublicationDate_xml | – month: 11 year: 2022 text: 2022-11-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Switzerland |
PublicationPlace_xml | – name: Switzerland – name: Basel |
PublicationTitle | Diagnostics (Basel) |
PublicationTitleAlternate | Diagnostics (Basel) |
PublicationYear | 2022 |
Publisher | MDPI AG MDPI |
Publisher_xml | – name: MDPI AG – name: MDPI |
References | ref_50 ref_14 ref_13 Kholiavchenko (ref_33) 2020; 15 ref_12 ref_11 Negassi (ref_18) 2020; 38 ref_10 ref_52 ref_51 ref_19 Fan (ref_24) 2020; 8 Ali (ref_2) 2021; 11 ref_17 ref_16 Shkolyar (ref_4) 2019; 76 ref_25 ref_23 ref_22 Shi (ref_40) 2021; 30 Yang (ref_7) 2020; 17 Ikeda (ref_8) 2019; 34 ref_29 ref_28 Polesel (ref_39) 2000; 9 ref_27 ref_26 Adelson (ref_34) 1983; 29 Yoo (ref_6) 2022; 12 Dice (ref_49) 1945; 26 ref_36 Karimi (ref_42) 2021; 116 ref_35 Malhotra (ref_31) 2022; 2022 ref_32 ref_30 Antoni (ref_1) 2016; 71 Hesamian (ref_20) 2019; 32 ref_37 Eminaga (ref_3) 2018; 2 He (ref_38) 2013; 35 ref_47 Minaee (ref_21) 2022; 44 ref_46 Lecun (ref_15) 1998; 86 ref_45 ref_44 ref_43 ref_41 ref_48 ref_9 ref_5 |
References_xml | – volume: 12 start-page: 17699 year: 2022 ident: ref_6 article-title: Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method publication-title: Sci. Rep. doi: 10.1038/s41598-022-22797-7 contributor: fullname: Yoo – volume: 26 start-page: 297 year: 1945 ident: ref_49 article-title: Measures of the Amount of Ecologic Association between Species publication-title: Ecology doi: 10.2307/1932409 contributor: fullname: Dice – volume: 71 start-page: 96 year: 2016 ident: ref_1 article-title: Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends publication-title: Eur. Urol. doi: 10.1016/j.eururo.2016.06.010 contributor: fullname: Antoni – ident: ref_32 – ident: ref_16 – volume: 35 start-page: 1397 year: 2013 ident: ref_38 article-title: Guided Image Filtering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.213 contributor: fullname: He – ident: ref_51 doi: 10.1109/WACV.2018.00163 – ident: ref_43 doi: 10.1109/ICCV.2017.324 – volume: 30 start-page: 7472 year: 2021 ident: ref_40 article-title: Unsharp Mask Guided Filtering publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2021.3106812 contributor: fullname: Shi – ident: ref_35 – volume: 86 start-page: 2278 year: 1998 ident: ref_15 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE doi: 10.1109/5.726791 contributor: fullname: Lecun – ident: ref_29 doi: 10.1007/978-3-030-01234-2_49 – ident: ref_26 doi: 10.1109/CVPR.2017.106 – ident: ref_27 – ident: ref_37 doi: 10.1109/CVPR.2017.195 – ident: ref_52 – volume: 76 start-page: 714 year: 2019 ident: ref_4 article-title: Augmented Bladder Tumor Detection Using Deep Learning publication-title: Eur. Urol. doi: 10.1016/j.eururo.2019.08.032 contributor: fullname: Shkolyar – volume: 8 start-page: 179656 year: 2020 ident: ref_24 article-title: MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3025372 contributor: fullname: Fan – ident: ref_48 – volume: 11 start-page: 11629 year: 2021 ident: ref_2 article-title: Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors publication-title: Sci. Rep. doi: 10.1038/s41598-021-91081-x contributor: fullname: Ali – volume: 15 start-page: 425 year: 2020 ident: ref_33 article-title: Contour-aware multi-label chest X-ray organ segmentation publication-title: Int. J. Comput. Assist. Radiol. Surg. doi: 10.1007/s11548-019-02115-9 contributor: fullname: Kholiavchenko – volume: 2022 start-page: 9580991 year: 2022 ident: ref_31 article-title: Deep Neural Networks for Medical Image Segmentation publication-title: J. Healthc. Eng. doi: 10.1155/2022/9580991 contributor: fullname: Malhotra – ident: ref_17 – ident: ref_45 – volume: 32 start-page: 582 year: 2019 ident: ref_20 article-title: Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges publication-title: J. Digit. Imaging doi: 10.1007/s10278-019-00227-x contributor: fullname: Hesamian – ident: ref_28 – ident: ref_22 doi: 10.1007/978-3-319-24574-4_28 – ident: ref_30 – ident: ref_5 doi: 10.1007/978-981-15-5199-4_6 – volume: 17 start-page: e2194 year: 2020 ident: ref_7 article-title: Automatic recognition of bladder tumours using deep learning technology and its clinical application publication-title: Int. J. Med Robot. Comput. Assist. Surg. doi: 10.1002/rcs.2194 contributor: fullname: Yang – ident: ref_47 – ident: ref_25 doi: 10.1109/VCIP.2017.8305148 – ident: ref_11 – ident: ref_9 doi: 10.1109/CVPR.2015.7298594 – ident: ref_13 doi: 10.1109/CVPR.2018.00474 – volume: 2 start-page: 1 year: 2018 ident: ref_3 article-title: Diagnostic Classification of Cystoscopic Images Using Deep Convolutional Neural Networks publication-title: JCO Clin. Cancer Inform. doi: 10.1200/CCI.17.00126 contributor: fullname: Eminaga – ident: ref_10 doi: 10.1109/CVPR.2009.5206848 – volume: 116 start-page: 102078 year: 2021 ident: ref_42 article-title: Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2021.102078 contributor: fullname: Karimi – ident: ref_44 – ident: ref_23 doi: 10.1007/978-3-030-00889-5_1 – ident: ref_19 doi: 10.2139/ssrn.4137336 – ident: ref_41 doi: 10.1109/ICCV.2019.00140 – ident: ref_50 – ident: ref_46 – ident: ref_12 doi: 10.1109/CVPR.2016.90 – volume: 9 start-page: 505 year: 2000 ident: ref_39 article-title: Image enhancement via adaptive unsharp masking publication-title: IEEE Trans. Image Process. doi: 10.1109/83.826787 contributor: fullname: Polesel – ident: ref_14 doi: 10.1109/CVPR.2016.308 – volume: 44 start-page: 3523 year: 2022 ident: ref_21 article-title: Image Segmentation Using Deep Learning: A Survey publication-title: IEEE Trans. Pattern Anal. Mach. Intell. contributor: fullname: Minaee – ident: ref_36 – volume: 38 start-page: 2349 year: 2020 ident: ref_18 article-title: Application of artificial neural networks for automated analysis of cystoscopic images: A review of the current status and future prospects publication-title: World J. Urol. doi: 10.1007/s00345-019-03059-0 contributor: fullname: Negassi – volume: 34 start-page: 352 year: 2019 ident: ref_8 article-title: Support system of cystoscopic diagnosis for bladder cancer based on artificial intelligence publication-title: J. Endourol. doi: 10.1089/end.2019.0509 contributor: fullname: Ikeda – volume: 29 start-page: 33 year: 1983 ident: ref_34 article-title: Pyramid Methods in Image Processing publication-title: RCA Eng. contributor: fullname: Adelson |
SSID | ssj0000913825 |
Score | 2.2888134 |
Snippet | Bladder cancer is a common and often fatal disease. Papillary bladder tumors are well detectable using cystoscopic imaging, but small or flat lesions are... |
SourceID | doaj pubmedcentral proquest gale crossref pubmed |
SourceType | Open Website Open Access Repository Aggregation Database Index Database |
StartPage | 2849 |
SubjectTerms | Accuracy Bladder cancer Classification convolutional neural network Cystoscopy Deep learning Diagnosis Excision (Surgery) guided filtering Image segmentation Light Localization Medical diagnosis Methods Neural networks semantic segmentation Semantics Technology application transfer learning Tumors Urology white-light cystoscopy |
SummonAdditionalLinks | – databaseName: Directory of Open Access Journals - May need to register for free articles dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9VAFB6kC3Ejvk2tMoLgxtBkMs_lbbUUoV1oC90N84oteHPLfSzc-8M9Zya93qDgxl3ITELmvOY75JxvCHkne5mEgzSVuzbVnJu-din7ldOGS-b7fNjE2bk8veSfr8TVzlFfWBNW6IGL4A5jF1QUJnWKOZ5i4_teda3wBjPxNpU-8lbsJFM5Bhvk1hOFZqiDvP4wlso15D5GVjOIymayFWXG_j_j8s7GNC2a3NmFTh6RhyN8pLPy2Y_JvTQ8IffPxh_kT8nPGT3-zedNsUrwB1309GNKtxSJOODh81L5vaKAV-kXAIo19oHQr2kOUr4JcPFtPnYkDbS0MVKAiTRva5tlWl_jW7BkL_dE4OuPvmMAW9KLzXyxXD0jlyefLo5P6_GghToAmlvXQfkktQge0hkAeK3hIXLea8NUG5CAXncSsmjWCEAMnjfea5aUdIKlxsfgu-dkb1gM6SWh0scYfdP63kA4SM4JrXpvdIzM4VhFPtzJ3N4WPg0LeQiqyP5FRRU5Qr1spyIZdr4BJmJHE7H_MpGKvEetWnRZUF1wY-cBfDGSX9mZwtZugM6iIgeTmeBqYTp8Zxd2dPWVhbReM4hqqqnI2-0wPonla0NabPIckB0HsFWRF8WMtkvqMAM0DSxVTQxssubpyHBznYnAjdSA9tj-_xDSK_KAYWdHbrM8IHvr5Sa9Bry19m-ya_0Cms8rdA priority: 102 providerName: Directory of Open Access Journals – databaseName: AUTh Library subscriptions: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1La9wwEBZtAqWX0nedpkWFQi81sWXJsk5lN00IhSwlTSA3o5eTQNfeencPufeHd0bWbmJaeltWsrE0D30jzXwi5GPZlF5oCFO5zn3KuWpS7YNd6UrxkpkmXDZxOitPLvi3S3EZN9yWMa1y4xODo3adxT3yA4ieKgbKI7Mvi18p3hqFp6vxCo2HZJflHI9pd6dHs-9n210WZL2EGGigGyogvj9wQwYbciAjuxl4ZzVakgJz_9_--d4CNU6evLcaHT8lTyKMpJNB7s_IA98-J49O40H5C_J7Qg_veL0pZgve0q6hX71fUCTkgIdnQwb4kgJupWcAGFOsB6E__Bxm-8bCj6t5rExq6VDOSAEu0rC8rXu_usa3YOpeqI3A109_oiPr6fl63vXLl-Ti-Oj88CSNFy6kFlDdKrXS-LIS1kBYA0AvV9w6zptKMZlbJKKvihKiaZYJQA6GZ8ZUzMtSC-Yz46wpXpGdtmv9G0JL45wzWW4aBW7Bay0q2RhVOcc0tiXk82bO68XAq1FDPIIiqv8hooRMUS7brkiKHf7o-qs62ljtCiudUL6QTHPvMtM0ssiFUbhpk3uZkE8o1RpNF0RndaxAgC9GEqx6IrHEGyC0SMj-qCeYnB03b_Sijia_rO8UNCEfts34JKaxtb5bhz4wdxxAV0JeD2q0HVKBkaDKYKhypGCjMY9b2pvrQAiuygpQH9v7_2e9JY8Z1m6EQsp9srPq1_4dIKqVeR_N5g-eQSRE priority: 102 providerName: ProQuest – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Nb9MwFLfGkBAXxDdhAxkJiQuBxHFi-4BQN5gmpO4Aq7Sb5a9sk9ZkpK203fnDec9JyyLGgVtVO1bs9_V77Xs_E_K2qqtQGkhTuclDyrmqUxOiXRmpeMVsHS-bmB5VhzP-7aQ82SLrW1GHA1zcmtrhfVKz7uLD1c_rz2DwnzDjhJT9o--L0pDWGAnLwOGqO-Qu4wVHlZ8OeD-6ZoWUe1jWyCBUwqvlsmci-tc6o2gVSf3_dt03Yte4rvJGoDp4SB4MCJNOepV4RLZC85jcmw7_oT8hvyZ0_w_lN8VCwmva1vRLCJcUuTrg4aO-OHxBAdLS74AlU2wVoT_CHARx7uDD6XxoWmpo3-lIAUnSGPlWXVie4SpY1RfbJnD5vQv0cR09Xs3bbvGUzA6-Hu8fpsNdDKkDwLdMnbChkqWzkPEABswVd57zWiomcocc9bKoINFmWQmgwvLMWsmCqEzJQma9s8Uzst20TXhBaGW99zbLba3AYwRjSilqq6T3zOBYQt6vz1xf9pQbGlIVFJG-RUQJ2UO5bKYiX3b8ou1O9WB-2hdO-FKFQjDDg89sXYsiL63C33PyIBLyDqWqUc9AdM4MzQnwxsiPpScCu78BXZcJ2R3NBGt04-G1Xui1MmvI_CUDxyeyhLzZDOOTWOHWhHYV58DZccBjCXneq9FmSwUmiSqDrYqRgo32PB5pzs8iV7iqJABC9vL_znSH3GfY5hF7LnfJ9rJbhVcAvpb2dTSo3yw3L-0 priority: 102 providerName: Scholars Portal |
Title | A Comparative Study of Deep Neural Networks for Real-Time Semantic Segmentation during the Transurethral Resection of Bladder Tumors |
URI | https://www.ncbi.nlm.nih.gov/pubmed/36428909 https://www.proquest.com/docview/2748279470 https://search.proquest.com/docview/2740504201 https://pubmed.ncbi.nlm.nih.gov/PMC9689102 https://doaj.org/article/d3c7d59e372a4ed0bff7315b993301e7 |
Volume | 12 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1La9wwEBZJCqWX0nfcposKhV7qrB-SJR13twmhsEtIE8jNWA8nC7G97OOQe354Z2Tvdk176sUYSzKWRjPzjT3zmZCvWZk5XkCYyorYhYypMiyc16tCKpYluvQ_m5jOsosb9vOW3x4Qvq2F8Un7Rs9P64fqtJ7f-9zKRWWG2zyx4eV0ojIJXi4ZHpJDkaZ7Ibo3vwpp9XjLMJRCSD-0bdIa0h4joRkYZNXzQp6s_2-TvOeT-vmSew7o_BV52SFHOmqf8DU5cPUb8nzafRt_S55GdPKHyptiguAjbUr6w7kFRQ4OGDxrk75XFKAqvQKMGGIJCP3lKljguYGTu6orRqppW8FIASFS79E2S7e-x7tgtp4vh8Dbjx_Qdi3p9aZqlqt35Ob87HpyEXb_WAgNALl1aIR2meRGQyQD2C5WzFjGSqkSERvknpdpBgF0EnEAC5pFWsvEiazgiYu0NTp9T47qpnbHhGbaWqujWJcKLIErCi5FqZW0NimwLSDft2ueL1oqjRxCEBRR_g8RBWSMctl1RR5sf6FZ3uXdbshtaoTlyqUiKZizkS5LkcZcK3xPEzsRkG8o1Ry1FURniq7oAJ4Yea_ykcCqbkDNPCAnvZ6gZabfvN0XeaflqxwiepmAQRNRQL7smnEkZq7Vrtn4PrB2DHBWQD6022g3pRSDPxXBVEVvg_Xm3G8BlfAc4J0KfPzvkZ_IiwQrOXxZ5Qk5Wi837jPgq7UekGfjs9nl1cC_n4DjlMmB17Hf5Msthg |
link.rule.ids | 230,315,733,786,790,870,891,2115,2236,21416,24346,27955,27956,33777,33778,43838,53825,53827,74657 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELZgKwEXxJtAASMhcSFq4jhxfEK7pdUC3RUqW6k3y6-0ldhk2ceBOz-cmcS7bQTiFsVOFHse_saZ-UzIu6IqfK4hTOU69THnsoq1b-1Kl5IXzFTtYROTaTE-41_O8_Ow4bYKaZVbn9g6atdY3CM_gOipZKA8Ivm4-BnjqVH4dzUcoXGb7CHlZjkge6Oj6bfT3S4Lsl5CDNTRDWUQ3x-4LoMNOZCR3Qy8s-wtSS1z_9_--cYC1U-evLEaHT8g9wOMpMNO7g_JLV8_Incm4Uf5Y_J7SA-veb0pZgv-ok1FP3m_oEjIAQ9PuwzwFQXcSk8BMMZYD0K_-znM9pWFi4t5qEyqaVfOSAEu0nZ52yz9-hLfgql7bW0Evn70Ax3Zks4282a5ekLOjo9mh-M4HLgQW0B169gK44sytwbCGgB6qeTWcV6VkonUIhF9mRUQTbMkB-RgeGJMybwodM58Ypw12VMyqJvaPye0MM45k6SmkuAWvNZ5KSojS-eYxraIfNjOuVp0vBoK4hEUkfqHiCIyQrnsuiIpdnujWV6oYGPKZVa4XPpMMM29S0xViSzNjcRNm9SLiLxHqSo0XRCd1aECAb4YSbDUUGCJN0DoPCL7vZ5gcrbfvNULFUx-pa4VNCJvd834JKax1b7ZtH1g7jiArog869RoN6QMI0GZwFBFT8F6Y-631FeXLSG4LEpAfezF_z_rDbk7nk1O1Mnn6deX5B7DOo62qHKfDNbLjX8F6GptXgcT-gOY1Sc6 |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1La9wwEBZtAqGX0necpq0KhV5q1pYlyzqV3SRL-sgS0gRyE9bDSaBrb_Zx6L0_vDO2dhPT0puxZGNpHvpGnvlEyIe8yr0oIUzlZepjzlUVl761q7JQPGemag-bOJnkxxf866W4DPlPi5BWufaJraN2jcU98gFETwUD5ZHJoAppEaeH48-z2xhPkMI_reE4jYdkW_JcQCC2PTqanJ5tdlyQARPioY56KINYf-C6bDbkQ0amM_DUqrc8tSz-f_vqe4tVP5Hy3so0fkIeB0hJh50OPCUPfP2M7JyEn-bPye8hPbjj-KaYOfiLNhU99H5GkZwDHp502eALChiWngF4jLE2hP7wU5j5GwsXV9NQpVTTrrSRAnSk7VK3mvvlNb4F0_jaOgl8_egnOrU5PV9Nm_niBbkYH50fHMfh8IXYAsJbxlYanxfCGghxAPSlilvHeVUoJlOLpPRFlkNkzRIBKMLwxJiCeZmXgvnEOGuyl2Srbmq_S2hunHMmSU2lwEX4shSFrIwqnGMltkXk03rO9azj2NAQm6CI9D9EFJERymXTFQmy2xvN_EoHe9Mus9IJ5TPJSu5dYqpKZqkwCjdwUi8j8hGlqtGMQXS2DNUI8MVIiKWHEsu9AU6LiOz3eoL52X7zWi90MP-FvlPWiLzfNOOTmNJW-2bV9oG54wDAIvKqU6PNkDKMClUCQ5U9BeuNud9S31y35OAqLwABsr3_f9Y7sgPWo79_mXx7TR4xLOlo6yv3ydZyvvJvAGgtzdtgQX8AUIQrbg |
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=A+Comparative+Study+of+Deep+Neural+Networks+for+Real-Time+Semantic+Segmentation+during+the+Transurethral+Resection+of+Bladder+Tumors&rft.jtitle=Diagnostics+%28Basel%29&rft.au=Varny%C3%BA%2C+D%C3%B3ra&rft.au=Szirmay-Kalos%2C+L%C3%A1szl%C3%B3&rft.date=2022-11-01&rft.issn=2075-4418&rft.eissn=2075-4418&rft.volume=12&rft.issue=11&rft.spage=2849&rft_id=info:doi/10.3390%2Fdiagnostics12112849&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_diagnostics12112849 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2075-4418&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2075-4418&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2075-4418&client=summon |