Automated analysis of cancer histopathology images

We explore image encoding and classification models to perform predictions on various types of tumor histopathology images. The questions we are interested in is tumor detection, tumor type classification, as well as prediction of therapy response. Beyond mere classification, we use attention mechanism to gain insights into the visual information that is key for a given classification task. Based on segmentation and staining we aim to provide interpretable cues on which cell types, their spatial distributions and cell-cell interaction might be specific to certain tumor types or predictive of patient clinical outcomes.