  Deep learning has recently gained popularity in digital pathology due to its
high prediction quality. However, the medical domain requires explanation and
insight for a better understanding beyond standard quantitative performance
evaluation. Recently, explanation methods have emerged, which are so far still
rarely used in medicine. This work shows their application to generate heatmaps
that allow to resolve common challenges encountered in deep learning-based
digital histopathology analyses. These challenges comprise biases typically
inherent to histopathology data. We study binary classification tasks of tumor
tissue discrimination in publicly available haematoxylin and eosin slides of
various tumor entities and investigate three types of biases: (1) biases which
affect the entire dataset, (2) biases which are by chance correlated with class
labels and (3) sampling biases. While standard analyses focus on patch-level
evaluation, we advocate pixel-wise heatmaps, which offer a more precise and
versatile diagnostic instrument and furthermore help to reveal biases in the
data. This insight is shown to not only detect but also to be helpful to remove
the effects of common hidden biases, which improves generalization within and
across datasets. For example, we could see a trend of improved area under the
receiver operating characteristic curve by 5% when reducing a labeling bias.
Explanation techniques are thus demonstrated to be a helpful and highly
relevant tool for the development and the deployment phases within the life
cycle of real-world applications in digital pathology.
