  Definitive cancer diagnosis and management depend upon the extraction of
information from microscopy images by pathologists. These images contain
complex information requiring time-consuming expert human interpretation that
is prone to human bias. Supervised deep learning approaches have proven
powerful for classification tasks, but they are inherently limited by the cost
and quality of annotations used for training these models. To address this
limitation of supervised methods, we developed Histomorphological Phenotype
Learning (HPL), a fully blue{self-}supervised methodology that requires no
expert labels or annotations and operates via the automatic discovery of
discriminatory image features in small image tiles. Tiles are grouped into
morphologically similar clusters which constitute a library of
histomorphological phenotypes, revealing trajectories from benign to malignant
tissue via inflammatory and reactive phenotypes. These clusters have distinct
features which can be identified using orthogonal methods, linking histologic,
molecular and clinical phenotypes. Applied to lung cancer tissues, we show that
they align closely with patient survival, with histopathologically recognised
tumor types and growth patterns, and with transcriptomic measures of
immunophenotype. We then demonstrate that these properties are maintained in a
multi-cancer study. These results show the clusters represent recurrent host
responses and modes of tumor growth emerging under natural selection. Code,
pre-trained models, learned embeddings, and documentation are available to the
community at
https://github.com/AdalbertoCq/Histomorphological-Phenotype-Learning

Computer Vision and Pattern Recognition;Machine Learning