  We present a novel approach to train pixel resolution segmentation models on
whole slide images in a weakly supervised setup. The model is trained to
classify patches extracted from slides. This leads the training to be made
under noisy labeled data. We solve the problem with two complementary
strategies. First, the patches are sampled online using the model's knowledge
by focusing on regions where the model's confidence is higher. Second, we
propose an extension of the KL divergence that is robust to noisy labels. Our
preliminary experiment on CAMELYON 16 data set show promising results. The
model can successfully segment tumor areas with strong morphological
consistency.
