  With the increase in the use of deep learning for computer-aided diagnosis in
medical images, the criticism of the black-box nature of the deep learning
models is also on the rise. The medical community needs interpretable models
for both due diligence and advancing the understanding of disease and treatment
mechanisms. In histology, in particular, while there is rich detail available
at the cellular level and that of spatial relationships between cells, it is
difficult to modify convolutional neural networks to point out the relevant
visual features. We adopt an approach to model histology tissue as a graph of
nuclei and develop a graph convolutional network framework based on attention
mechanism and node occlusion for disease diagnosis. The proposed method
highlights the relative contribution of each cell nucleus in the whole-slide
image. Our visualization of such networks trained to distinguish between
invasive and in-situ breast cancers, and Gleason 3 and 4 prostate cancers
generate interpretable visual maps that correspond well with our understanding
of the structures that are important to experts for their diagnosis.
