Explainable AI for pathology

Histopathology has been used as a very important resource for diagnosis so far. If a lot of By utilizing the accumulated pathological data risk predictions in addition to conventional diagnoses can be made. To predict precise prognostic pathological features, we introduced tumor environment-associated context learning using graph deep learning (TEA-graph), using whole slide images and linked survival information. Using graph neural networks (GNNs), the local pathological features were analyzed with the contextual features reflecting their surrounding environment in whole slide images. TEA-graph improved risk prediction and stratification performance, which would aid both clinicians and patients in real-life settings.

• Yongju Lee, Jeong Hwan Park, Sohee Oh, Kyoungseob Shin, Jiyu Sun, Minsun Jung, Cheol Lee, Hyojin Kim, Jin-Haeng Chung, Kyung Chul Moon & Sunghoon Kwon (2022). Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning. Nature Biomedical Engineering