RESEARCH SEMINAR:  Representation Learning Under Weak Supervision in Computational Pathology

Guest : Dr. Tolga Taşdizen

Title : Representation Learning Under Weak Supervision in Computational Pathology

Date / Time : 6 July 2026, 11:40

Location : FENS L055

Abstract : Computational pathology has advanced rapidly with deep learning and, more recently, pathology foundation models that provide strong transferable representations from whole-slide images. Yet important gaps remain: pretrained features often retain domain shift relative to downstream clinical datasets, and most existing pipelines do not explicitly model the geometric organization of tissue architecture that underlies disease progression. In this talk, I will present our work on weak- and semi-supervised representation learning methods designed to address these challenges, including adaptive stain separation for contrastive learning, bag-label-aware contrastive pretraining for multiple-instance learning, and distance-aware spatial modeling that injects tissue geometry into slide-level prediction. These methods reduce dependence on dense annotations while improving the quality, robustness, and clinical relevance of learned representations in histopathology. Across kidney and prostate cancer studies, they produce stronger downstream performance than standard self-supervised, semi-supervised, and MIL baselines, including improved classification on ccRCC datasets and more accurate prediction of metastatic risk from diagnostic prostate biopsies.

Bio:  Dr. Tasdizen is Professor and Associate Chair of Electrical and Computer Engineering and a faculty member of the Scientific Computing and Imaging Institute at the University of Utah, where he works on AI and machine learning for image analysis with applications in biomedical imaging, public health, and materials science. His research spans self- and semi-supervised learning, domain adaptation, and interpretability.