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      Automated acquisition of explainable knowledge from unannotated histopathology images

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          Abstract

          Deep learning algorithms have been successfully used in medical image classification. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Here we show that deep learning algorithm enables automated acquisition of explainable features from diagnostic annotation-free histopathology images. We compare the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by expert pathologists using established criteria on 13,188 whole-mount pathology images consisting of over 86 billion image patches. Our method not only reveals findings established by humans but also features that have not been recognized, showing higher accuracy than human in prognostic prediction. Combining both our algorithm-generated features and human-established criteria predicts the recurrence more accurately than using either method alone. We confirm robustness of our method using external validation datasets including 2276 pathology images. This study opens up fields of machine learning analysis for discovering uncharted knowledge.

          Abstract

          Technologies for acquiring explainable features from medical images need further development. Here, the authors report a deep learning based automated acquisition of explainable features from pathology images, and show a higher accuracy of their method as compared to pathologist based diagnosis of prostate cancer recurrence.

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          Most cited references 10

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          Ridge Regression: Biased Estimation for Nonorthogonal Problems

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            Machine Learning Methods for Histopathological Image Analysis

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              Pathologist workforce in the United States: I. Development of a predictive model to examine factors influencing supply.

              Results of prior pathology workforce surveys have varied between a state of equilibrium and predictions of shortage.
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                Author and article information

                Contributors
                yoichiro.yamamoto@riken.jp
                gokimura@nms.ac.jp
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 December 2019
                18 December 2019
                2019
                : 10
                Affiliations
                [1 ]ISNI 0000000094465255, GRID grid.7597.c, Pathology Informatics Team, , RIKEN Center for Advanced Intelligence Project, ; Nihonbashi 1-chome Mitsui Bldg. 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
                [2 ]ISNI 0000 0001 1507 4692, GRID grid.263518.b, Department of Pathology, , Shinshu University School of Medicine, ; 3-1-1 Asahi, Matsumoto, Nagano, 390-8621 Japan
                [3 ]ISNI 0000 0001 0727 1557, GRID grid.411234.1, Department of Surgical Pathology, , Aichi Medical University Hospital, ; 1-1 Yazakokarimata, Nagakute, Aichi, 480-1195 Japan
                [4 ]ISNI 0000 0004 0616 2203, GRID grid.416279.f, Department of Urology, , Nippon Medical School Hospital, ; 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8603 Japan
                [5 ]ISNI 0000000094465255, GRID grid.7597.c, Statistical Genetics Team, , RIKEN Center for Advanced Intelligence Project, ; Nihonbashi 1-chome Mitsui Bldg. 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
                [6 ]ISNI 0000 0004 0372 3116, GRID grid.412764.2, Department of Urology, , St. Marianna University School of Medicine, ; 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511 Japan
                [7 ]ISNI 0000 0001 2173 8328, GRID grid.410821.e, Department of Analytic Human Pathology, , Nippon Medical School, ; 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8603 Japan
                [8 ]ISNI 0000 0004 0372 3116, GRID grid.412764.2, Department of Pathology, , St. Marianna University School of Medicine, ; 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511 Japan
                [9 ]Diagnostic Pathology, Ritsuzankai Iida Hospital, 1-15 Odori, Iida, Nagano, 395-8505 Japan
                [10 ]ISNI 0000 0001 2248 6943, GRID grid.69566.3a, Institute of Development, Aging and Cancer, , Tohoku University, ; 4-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575 Japan
                [11 ]ISNI 0000 0001 2248 6943, GRID grid.69566.3a, Tohoku Medical Megabank Organization, , Tohoku University, ; 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573 Japan
                [12 ]ISNI 0000000094465255, GRID grid.7597.c, Goal-Oriented Technology Research Group, , RIKEN Center for Advanced Intelligence Project, ; Nihonbashi 1-chome Mitsui Bldg. 15F, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
                Article
                13647
                10.1038/s41467-019-13647-8
                6920352
                31852890
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: This research was supported by the ICT Infrastructure for the Establishment and Implementation of Artificial Intelligence for Clinical and Medical Research of the Japan Agency for Medical Research and development, AMED, and the Centre for Advanced Intelligence Project, RIKEN.
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                © The Author(s) 2019

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                cancer imaging, translational research, computer science

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