Self-supervised learning in non-small cell lung cancer discovers novel morphological clusters linked to patient outcome and molecular phenotypes. (arXiv:2205.01931v2 [cs.CV] UPDATED)

Histopathological images provide the definitive source of cancer diagnosis,
containing information used by pathologists to identify and subclassify
malignant disease, and to guide therapeutic choices. These images contain vast
amounts of information, much of which is currently unavailable to human
interpretation. Supervised deep learning approaches have been powerful for
classification tasks, but they are inherently limited by the cost and quality
of annotations. Therefore, we developed Histomorphological Phenotype Learning,
an unsupervised methodology, which requires no annotations and operates via the …

arxiv cancer cv learning lung cancer patient self-supervised learning small supervised learning