Figure 4.

Illustration of the effects of the three studied types of biases on high-resolution explanation heatmaps. These are contrasted against heatmaps of models which are not affected by the biases (right). Dataset bias. This dataset is characterised by a label bias which results from determining the label solely from the patch’s centre cell (yellow mark). The heatmap demonstrates how the network therefore learns to focus on the centre of the patch. Class-correlated bias. Detection of a class related bias in form of a small artificial corruption. The heatmap reveals that the model has based its decision on the bias instead of relevant biological features. High-resolution heatmaps are able to identify these class-correlated biases in a single example and to accurately pinpoint to even very small artefacts. Sample bias. Demonstrating the effect of sampling biases by training a classifier on a dataset lacking examples of necrosis. The presented exemplary tile presents, apart from necrotic tissue (yellow annotations), also cancer cells to show the correct classification of the target class in both cases while the assessment of necrotic tissue differs between the classifiers.