Figure 1.

Method overview. (a) An example of poorly differentiated ADC annotation that was carried out on the WSIs by pathologists using in an in-house web-based application. (b) The initial training consisted in fully-random balanced sampling of positive (poorly differentiated ADC) and negative tiles to fine-tune the models. (c) After a few epochs of random sampling, the training switched into iterative hard mining of tiles that alternates between training and inference. During the inference step, we applied the model in a sliding window fashion on all of the WSI and selected the k tiles with the highest probabilities if the WSI was negative, and k tiles with the lowest probabilities if the WSI was positive. The tiles were collected in a subset that was batched and used for training. This process allows training to reduce false positives.