Nature Communications (Dec 2019)
Automated acquisition of explainable knowledge from unannotated histopathology images
- Yoichiro Yamamoto,
- Toyonori Tsuzuki,
- Jun Akatsuka,
- Masao Ueki,
- Hiromu Morikawa,
- Yasushi Numata,
- Taishi Takahara,
- Takuji Tsuyuki,
- Kotaro Tsutsumi,
- Ryuto Nakazawa,
- Akira Shimizu,
- Ichiro Maeda,
- Shinichi Tsuchiya,
- Hiroyuki Kanno,
- Yukihiro Kondo,
- Manabu Fukumoto,
- Gen Tamiya,
- Naonori Ueda,
- Go Kimura
Affiliations
- Yoichiro Yamamoto
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project
- Toyonori Tsuzuki
- Department of Surgical Pathology, Aichi Medical University Hospital
- Jun Akatsuka
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project
- Masao Ueki
- Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project
- Hiromu Morikawa
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project
- Yasushi Numata
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project
- Taishi Takahara
- Department of Surgical Pathology, Aichi Medical University Hospital
- Takuji Tsuyuki
- Department of Surgical Pathology, Aichi Medical University Hospital
- Kotaro Tsutsumi
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project
- Ryuto Nakazawa
- Department of Urology, St. Marianna University School of Medicine
- Akira Shimizu
- Department of Analytic Human Pathology, Nippon Medical School
- Ichiro Maeda
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project
- Shinichi Tsuchiya
- Diagnostic Pathology, Ritsuzankai Iida Hospital
- Hiroyuki Kanno
- Department of Pathology, Shinshu University School of Medicine
- Yukihiro Kondo
- Department of Urology, Nippon Medical School Hospital
- Manabu Fukumoto
- Pathology Informatics Team, RIKEN Center for Advanced Intelligence Project
- Gen Tamiya
- Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project
- Naonori Ueda
- Goal-Oriented Technology Research Group, RIKEN Center for Advanced Intelligence Project
- Go Kimura
- Department of Urology, Nippon Medical School Hospital
- DOI
- https://doi.org/10.1038/s41467-019-13647-8
- Journal volume & issue
-
Vol. 10,
no. 1
pp. 1 – 9
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.
