Automated acquisition of explainable knowledge from unannotated histopathology images.

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 algori

SUBMITTER: Yamamoto Y 

PROVIDER: S-EPMC6920352 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature