Self-supervised learning in non-small cell lung cancer discovers novel morphological clusters linked to patient outcome and molecular phenotypes
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 …
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 …
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