Overview of training method.

(a) shows a zoomed-in example of a tile from a WSI. (b) During training, we alternated between an inference step and a training step. During the inference step, the model weights were frozen and the model was used to select tiles with the highest probability after applying it on the entire tissue regions of each WSI. The top k tiles with the highest probabilities were then selected from each WSI and placed into a queue. During training, the selected tiles from multiple WSIs formed a training batch and were used to train the model.