Addressing the Key Computational Challenges in Digital Pathology

This project aims to tackle key computational challenges in digital pathology, including data scarcity, domain variability, long-tailed distributions, data imbalance, the need to ensure data privacy during model training, and high computational demands. To address these issues, we will employ a range of advanced machine learning techniques, including weakly supervised / semi-supervised / unsupervised learning, generative AI, meta-learning, transfer learning, prototype learning and prompt learning.