Скачать или смотреть Webinar #5: Data efficient and multimodal computational pathology - Prof. Faisal Mahmood

Title: Data-efficient and multimodal computational pathology

Abstract: Advances in digital pathology and artificial intelligence have presented the potential to build assistive tools for objective diagnosis, prognosis and therapeutic-response and resistance prediction. In this talk we will discuss: 1) Data-efficient methods for weakly-supervised whole slide classification with examples in cancer diagnosis and subtyping, allograft rejection etc. (Nature Biomedical Engineering, 2021). 2) Harnessing weakly-supervised, fast and data-efficient WSI classification for identifying origins for cancers of unknown primary (Nature, 2021). 3) Discovering integrative histology-genomic prognostic markers via interpretable multimodal deep learning (IEEE TMI, 2020). 4) Deploying weakly supervised models in low resource settings without slide scanners, network connections, computational resources and expensive microscopes. 5) Bias and fairness in computational pathology algorithms.

Bio: Dr. Mahmood is an Assistant Professor of Pathology at Harvard Medical School and the Division of Computational Pathology at the Brigham and Women's Hospital. He is also an Associate Member of the Broad Institute of Harvard and MIT, a member of the Harvard Bioinformatics and Integrative Genomics (BIG) faculty and a full member of the Dana-Farber / Harvard Cancer Center. His laboratory’s predominant focus is towards pathology image analysis, morphological feature, and biomarker discovery using data fusion and multimodal analysis.