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• W2914443871 abstract "Abstract Deep learning algorithms have been successfully used in medical image classification and cancer detection. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Herein, fully automated acquisition of explainable features from annotation-free histopathological images is achieved via revealing statistical distortions in datasets by introducing the way of pathologists’ examination into a set of deep neural networks. As validation, we compared the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by an expert pathologist using established criteria on 13,188 whole-mount pathology images. Our method found not only the findings established by humans but also features that have not been recognized so far, and showed higher accuracy than human in prognostic prediction. This study provides a new field to the deep learning approach as a novel tool for discovering uncharted knowledge, leading to effective treatments and drug discovery." @default.
• W2914443871 created "2019-02-21" @default.
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• W2914443871 date "2019-02-04" @default.
• W2914443871 modified "2023-09-23" @default.
• W2914443871 title "Automated acquisition of knowledge beyond pathologists" @default.
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• W2914443871 doi "https://doi.org/10.1101/539791" @default.
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