◦ : Build a Differentially Private Federated learning framework for Computational Pathology on Gigapixel WSI using Multiple Instance Learing Model.
◦ : Evaluate performance of the framework on the popular opensource TGCA dataset and comparing the performance between federated learning and standalone methods.
◦ : Creating a bespoke dataset in collaboration with NIMHANs and evaluate the frame work with a combination of TCGA and the bespoke dataset.