Learning Deep Representations of Cancer Tissue: Ke Yuan, 7th March 2022
Автор: TIA Warwick
Загружено: 2022-03-08
Просмотров: 563
TIA Seminar by Dr Ke Yuan, University of Glasgow
Abstract: Cancer is an evolutionary process characterised by heterogeneity between and within tumours. In this talk, I will discuss how unsupervised representation learning models can improve quantification of heterogeneity within histological images of tumour slides. These representation learning models include deep generative models and self-supervised models. Using images across Breast, Colon and Lung tumours, these models capture distinct phenotypic characteristics of tissue samples, including cancer cell destiny, tissue types, growth patterns and clinical outcome, paving the way for further understanding of tumour progression and tumour micro-environment, and ultimately refining histopathological classification for diagnosis and treatment.
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