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Self-supervised learning unveils morphological clusters behind lung cancer types and prognosis. (arXiv:2205.01931v1 [cs.CV])
cs.LG updates on arXiv.org arxiv.org
Histopathological images of tumors contain abundant information about how
tumors grow and how they interact with their micro-environment. Characterizing
and improving our understanding of phenotypes could reveal factors related to
tumor progression and their underpinning biological processes, ultimately
improving diagnosis and treatment. In recent years, the field of histological
deep learning applications has seen great progress, yet most of these
applications focus on a supervised approach, relating tissue and associated
sample annotations. Supervised approaches have their impact limited by two …
arxiv cancer cv learning lung cancer self-supervised learning supervised learning types