Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
IMPORTANCE: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. OBJECTIVE: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph no...
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| Published in | JAMA : the journal of the American Medical Association Vol. 318; no. 22; pp. 2199 - 2210 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Medical Association
12.12.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0098-7484 1538-3598 1538-3598 |
| DOI | 10.1001/jama.2017.14585 |
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| Abstract | IMPORTANCE: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. OBJECTIVE: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. DESIGN, SETTING, AND PARTICIPANTS: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). EXPOSURES: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. MAIN OUTCOMES AND MEASURES: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. RESULTS: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). CONCLUSIONS AND RELEVANCE: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting. |
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| AbstractList | Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.
Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting.
Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC).
Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation.
The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor.
The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC).
In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting. IMPORTANCE: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. OBJECTIVE: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. DESIGN, SETTING, AND PARTICIPANTS: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). EXPOSURES: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. MAIN OUTCOMES AND MEASURES: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. RESULTS: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). CONCLUSIONS AND RELEVANCE: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting. This diagnostic accuracy study compares the ability of machine learning algorithms vs clinical pathologists to detect cancer metastases in whole-slide images of axillary lymph nodes dissected from women with breast cancer. Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.ImportanceApplication of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting.ObjectiveAssess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting.Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC).Design, Setting, and ParticipantsResearcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC).Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation.ExposuresDeep learning algorithms submitted as part of a challenge competition or pathologist interpretation.The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor.Main Outcomes and MeasuresThe presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor.The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC).ResultsThe area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC).In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.Conclusions and RelevanceIn the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting. IMPORTANCE Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. OBJECTIVE Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. DESIGN, SETTING. AND PARTICIPANTS Researcher challenge competition (CAM ELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WIC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). EXPOSURES Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. MAIN OUTCOMES AND MEASURES The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. RESULTS The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; p < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). CONCLUSIONS AND RELEVANCE In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting. |
| Author | Valkonen, Mira Kartasalo, Kimmo Khvatkov, Vitali Latonen, Leena Kalinovsky, Alexander Liauchuk, Vitali Mungal, Bharti Litjens, Geert Bruni, Elia Seno, Shigeto Qaiser, Talha Kovalev, Vassili Tsang, Yee-Wah Veta, Mitko van der Laak, Jeroen A. W. M Ehteshami Bejnordi, Babak Takenaka, Yoichi van Ginneken, Bram Stathonikos, Nikolaos Vylegzhanin, Alexei Watanabe, Seiryo Deniz, Oscar Tellez, David Wang, Dayong Wong, Quincy Ruusuvuori, Pekka Ahmady Phoulady, Hady Chen, Hao Beck, Andrew H Dou, Qi Öner, Mustafa Ümit Hermsen, Meyke Manson, Quirine F Li, Quanzheng Hufnagl, Peter Rajpoot, Nasir Karssemeijer, Nico Geessink, Oscar Zhong, Aoxiao van Dijk, Marcory CRF Kraus, Oren Heng, Pheng-Ann Halici, Ugur Albarqouni, Shadi Venâncio, Rui Matsuda, Hideo Awan, Ruqayya Demirci, Stefanie Johannes van Diest, Paul George, Ami Haß, Christian Berseth, Matt Racoceanu, Daniel Fernandez-Carrobles, M. Milagro Gargeya, Rishab Serrano, Ismael Lin, Huang-Jing Cetin-Atalay, Rengul Annuscheit, Jonas Shaban, Muhammad Irshad, Humayun Sirinukunwattana, Korsuk Bult, Peter Bueno, Glo |
| AuthorAffiliation | 29 Technical University of Munich, Munich, Germany 6 Rijnstate Hospital, Arnhem, the Netherlands 23 Department of Computer Science and Engineering, Qatar University, Doha, Qatar 18 NLP LOGIX, Jacksonville, Florida 36 Sorbonne University, Pierre and Marie Curie University, Paris, France 19 Smart Imaging Technologies, Houston, Texas 8 PathAI, Cambridge, Massachusetts 35 Pontifical Catholic University of Peru, San Miguel, Lima, Peru 32 Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus National Academy of Sciences, Minsk, Belarus 24 Hochschule für Technik und Wirtschaft, Berlin, Germany 33 Visilab, University of Castilla-La Mancha, Ciudad Real, Spain 13 ExB Research and Development GmbH, Munich, Germany 34 INSERM, Laboratoire d’Imagerie Biomédicale, Sorbonne Universiteś, Pierre and Marie Curie University, Paris, France 3 Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands 30 Department of Bioinformatic Engineering, Osaka University 11 |
| AuthorAffiliation_xml | – name: 10 Harker School, San Jose, California – name: 2 Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, the Netherlands – name: 23 Department of Computer Science and Engineering, Qatar University, Doha, Qatar – name: 26 BioMediTech Institute and Faculty of Biomedical Science and Engineering, Tampere University of Technology, Tampere, Finland – name: 15 Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey – name: 22 Department of Pathology, University Hospitals Coventry and Warwickshire National Health Service Foundation Trust, Coventry, United Kingdom – name: 29 Technical University of Munich, Munich, Germany – name: 33 Visilab, University of Castilla-La Mancha, Ciudad Real, Spain – name: 16 Neuroscience and Neurotechnology, Graduate School of Natural and Applied Sciences, Middle East Technical University, Ankara, Turkey – name: 11 Center for Clinical Data Science, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts – name: 17 Cancer System Biology Laboratory, Graduate School of Informatics, Middle East Technical University, Ankara, Turkey – name: 3 Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands – name: 35 Pontifical Catholic University of Peru, San Miguel, Lima, Peru – name: 1 Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands – name: 12 Chinese University of Hong Kong, Hong Kong, China – name: 34 INSERM, Laboratoire d’Imagerie Biomédicale, Sorbonne Universiteś, Pierre and Marie Curie University, Paris, France – name: 36 Sorbonne University, Pierre and Marie Curie University, Paris, France – name: 9 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts – name: 25 BioMediTech Institute and Faculty of Medicine and Life Sciences, Tampere University of Technology, Tampere, Finland – name: 6 Rijnstate Hospital, Arnhem, the Netherlands – name: 24 Hochschule für Technik und Wirtschaft, Berlin, Germany – name: 32 Biomedical Image Analysis Department, United Institute of Informatics Problems, Belarus National Academy of Sciences, Minsk, Belarus – name: 30 Department of Bioinformatic Engineering, Osaka University – name: 31 University of South Florida, Tampa, Florida – name: 21 Tissue Image Analytics Lab, Department of Computer Science, University of Warwick, Coventry, United Kingdom – name: 27 Prostate Cancer Research Center, Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland – name: 5 Laboratorium Pathologie Oost Nederland, Hengelo, the Netherlands – name: 20 Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada – name: 4 Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands – name: 7 BeckLab, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts – name: 13 ExB Research and Development GmbH, Munich, Germany – name: 19 Smart Imaging Technologies, Houston, Texas – name: 14 Munich Business School, Munich, Germany – name: 28 Faculty of Computing and Electrical Engineering, Tampere University of Technology, Pori, Finland – name: 18 NLP LOGIX, Jacksonville, Florida – name: 8 PathAI, Cambridge, Massachusetts |
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Milagro surname: Fernandez-Carrobles fullname: Fernandez-Carrobles, M. Milagro – sequence: 67 givenname: Ismael surname: Serrano fullname: Serrano, Ismael – sequence: 68 givenname: Oscar surname: Deniz fullname: Deniz, Oscar – sequence: 69 givenname: Daniel surname: Racoceanu fullname: Racoceanu, Daniel – sequence: 70 givenname: Rui surname: Venâncio fullname: Venâncio, Rui |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29234806$$D View this record in MEDLINE/PubMed |
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| References | 29234791 - JAMA. 2017 Dec 12;318(22):2184-2186 29542717 - Nature. 2018 Mar 15;555(7696):285 29234793 - JAMA. 2017 Dec 12;318(22):2250-2251 29392271 - JAMA Oncol. 2018 Mar 1;4(3):403-404 29710156 - JAMA. 2018 Apr 24;319(16):1725-1726 |
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| Snippet | IMPORTANCE: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. OBJECTIVE:... Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Assess the performance of... IMPORTANCE Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. OBJECTIVE Assess... Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.ImportanceApplication of... This diagnostic accuracy study compares the ability of machine learning algorithms vs clinical pathologists to detect cancer metastases in whole-slide images... |
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| SubjectTerms | Algorithms Breast cancer Breast Neoplasms - pathology Cancer Competition Computer simulation Data processing Deep learning Diagnostic systems Female Humans Image classification Learning Learning algorithms Lymph nodes Lymphatic Metastasis - diagnosis Lymphatic Metastasis - pathology Lymphatic system Machine Learning Medical diagnosis Metastases Metastasis Original Investigation Pathologists Pathology Pathology, Clinical ROC Curve Simulation Workflow |
| Title | Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer |
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