Automated acquisition of explainable knowledge from unannotated histopathology images

Automated acquisition of explainable knowledge from unannotated histopathology images

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 p...

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Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Science
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Acceso en línea:https://doaj.org/article/6ae4bd550e6b481f950398b8c01f372f
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Sumario: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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