Pathology Grand Rounds - Data-Efficient and Multimodal Computational Pathology
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
Type: LECTURE/TALK