During this presentation, learners will review:

• Data-efficient methods for weakly-supervised whole slide classification with examples in cancer diagnosis and subtyping, allograft rejection etc. (Nature Biomedical Engineering, 2021)
• Harnessing weakly-supervised, fast and data-efficient WSI classification for identifying origins for cancers of unknown primary (Nature, 2021)
• Discovering integrative histology-genomic prognostic markers via interpretable multimodal deep learning (IEEE TMI, 2020)
• Deploying weakly supervised models in low resource settings without slide scanners, network connections, computational resources and expensive microscopes
• Bias and fairness in computational pathology algorithms