Clinically Interpretable XAI for glioblastoma diagnosis

For my dissertation, I built a model for brain tumor diagnosis designed to be clinically interpretable. It builds on prior work with concept bottleneck models to integrate clinical concepts into the pipeline, which were defined in collaboration with clinicians.

I achieved an outstanding classification for this work (84%).