Maidinamu Yakufujiang, Yoshinori Higuchi, Kyoko Aoyagi, Tatsuya Yamamoto, Midori Abe, Yoji Okahara, Masaki Izumi, Osamu Nagano, Yoshitaka Yamanaka, Shigeki Hirano, Akihiro Shiina, Atsushi Murata, Yasuo Iwadate
Predictive potential of preoperative electroencephalogram for neuropsychological change following subthalamic nucleus deep brain stimulation in Parkinson's disease
Acta neurochirurgica
interpretability machine learning become increasingly important machine learning used applications including highstakes consequences healthcare interpretability regarded key successful adoption machine learning models however using confoundingirrelevant information making predictions deep learning models even interpretable ones poses critical challenges clinical acceptance recently drawn researchers attention issues beyond mere interpretation deep learning models paper first investigate application inherently interpretable prototypebased architecture known protopnet breast cancer classification digital pathology highlight shortcomings application propose new method uses medically relevant information makes accurate interpretable predictions method leverages clustering concept implicitly increases number classes training dataset proposed method learns relevant prototypes without pixellevel annotated data holistic assessment addition classification accuracy define new metric assessing degree interpretability based comments group skilled pathologists experimental results breakhis dataset show proposed method effectively improves classification accuracy interpretability respectively 8 18 therefore proposed method seen step toward implementing interpretable deep learning models detection breast cancer using histopathology images