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Deep Learning for Identifying Metastatic Breast Cancer
TLDR
The power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses is demonstrated, by combining the deep learning system's predictions with the human pathologist's diagnoses.Abstract:
The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. Our team won both competitions in the grand challenge, obtaining an area under the receiver operating curve (AUC) of 0.925 for the task of whole slide image classification and a score of 0.7051 for the tumor localization task. A pathologist independently reviewed the same images, obtaining a whole slide image classification AUC of 0.966 and a tumor localization score of 0.733. Combining our deep learning system's predictions with the human pathologist's diagnoses increased the pathologist's AUC to 0.995, representing an approximately 85 percent reduction in human error rate. These results demonstrate the power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses.read more
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Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
TL;DR: In this paper, the authors provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs, and discuss various hardware platforms and architectures that support DNN, and highlight key trends in reducing the computation cost of deep neural networks either solely via hardware design changes or via joint hardware and DNN algorithm changes.
Journal ArticleDOI
Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Michael Zietz,Michael M. Hoffman,Michael M. Hoffman,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,Evan M. Cofer,Christopher A. Lavender,Srinivas C. Turaga,Amr Alexandari,Zhiyong Lu,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Simina M. Boca,S. Joshua Swamidass,Austin Huang,Anthony Gitter,Anthony Gitter,Casey S. Greene +38 more
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
Journal ArticleDOI
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.
Gabriele Campanella,Gabriele Campanella,Matthew G. Hanna,Luke Geneslaw,Allen P. Miraflor,Vitor Werneck Krauss Silva,Klaus J. Busam,Edi Brogi,Victor E. Reuter,David S. Klimstra,Thomas J. Fuchs,Thomas J. Fuchs +11 more
TL;DR: A multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations, and has the ability to train accurate classification models at unprecedented scale.
Journal ArticleDOI
The practical implementation of artificial intelligence technologies in medicine.
TL;DR: The current regulatory environment in the United States is summarized and comparisons are highlighted with other regions in the world, notably Europe and China, to bring the full potential of AI to the clinic.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Proceedings Article
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Proceedings Article
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Proceedings ArticleDOI
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