Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network

1 code implementation • 26 Nov 2021 • Esther Dietrich, Patrick Fuhlert, Anne Ernst, Guido Sauter, Maximilian Lennartz, H. Siegfried Stiehl, Marina Zimmermann, Stefan Bonn

On the use case of prostate cancer survival prediction, using 14, 479 images and only relapse times as annotations, we reach a cumulative dynamic AUC of 0. 78 on a validation set, being on par with an expert pathologist (and an AUC of 0. 77 on a separate test set).

Multiple Instance Learning Survival Prediction