Attention-based Interpretable Regression of Gene Expression in Histology
This work addresses interpretability in medical imaging for patient stratification in pathology, but it is incremental as it builds on existing attention-based methods for a specific domain.
The paper tackled the problem of predicting gene expression from histology images in colorectal cancer, achieving successful identification of hotspots of high gene expression to estimate values for a subset of genes linked to cancer subtype, survival, and treatment response.
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient recommendations. For models exceeding human performance, e.g. predicting RNA structure from microscopy images, interpretable modelling can be further used to uncover highly non-trivial patterns which are otherwise imperceptible to the human eye. We show that interpretability can reveal connections between the microscopic appearance of cancer tissue and its gene expression profiling. While exhaustive profiling of all genes from the histology images is still challenging, we estimate the expression values of a well-known subset of genes that is indicative of cancer molecular subtype, survival, and treatment response in colorectal cancer. Our approach successfully identifies meaningful information from the image slides, highlighting hotspots of high gene expression. Our method can help characterise how gene expression shapes tissue morphology and this may be beneficial for patient stratification in the pathology unit. The code is available on GitHub.