Visualization for Histopathology Images using Graph Convolutional Neural Networks
This provides interpretable models for medical diagnosis, addressing the black-box criticism in histopathology, though it is incremental as it adapts existing graph and attention methods to a specific domain.
The paper tackled the problem of interpretability in deep learning for histopathology by modeling tissue as a graph of nuclei and using a graph convolutional network with attention and node occlusion to highlight cell contributions, generating visual maps that align with expert diagnostic structures for breast and prostate cancers.
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.