研究者情報

ナカザワ　リュウト   NAKAZAWA RYUTO   中澤龍斗    所属   医学部医学科 腎泌尿器外科学    職種   教授
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Automated acquisition of explainable knowledge from unannotated histopathology images.
掲載誌名	正式名：Nature communications 略　 称：Nat Commun ISSNコード：2041172320411723
掲載区分	国外
巻・号・頁	10(1),5642頁
著者・共著者	Yamamoto Yoichiro, Tsuzuki Toyonori, Akatsuka Jun, Ueki Masao, Morikawa Hiromu, Numata Yasushi, Takahara Taishi, Tsuyuki Takuji, Tsutsumi Kotaro, Nakazawa Ryuto, Shimizu Akira, Maeda Ichiro, Tsuchiya Shinichi, Kanno Hiroyuki, Kondo Yukihiro, Fukumoto Manabu, Tamiya Gen, Ueda Naonori, Kimura Go
発行年月	2019/12
概要	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.
DOI	10.1038/s41467-019-13647-8
PMID	31852890