  Tissue phenotyping is a fundamental computational pathology (CPath) task in
learning objective characterizations of histopathologic biomarkers in anatomic
pathology. However, whole-slide imaging (WSI) poses a complex computer vision
problem in which the large-scale image resolutions of WSIs and the enormous
diversity of morphological phenotypes preclude large-scale data annotation.
Current efforts have proposed using pretrained image encoders with either
transfer learning from natural image datasets or self-supervised pretraining on
publicly-available histopathology datasets, but have not been extensively
developed and evaluated across diverse tissue types at scale. We introduce UNI,
a general-purpose self-supervised model for pathology, pretrained using over
100 million tissue patches from over 100,000 diagnostic haematoxylin and
eosin-stained WSIs across 20 major tissue types, and evaluated on 33
representative CPath clinical tasks in CPath of varying diagnostic
difficulties. In addition to outperforming previous state-of-the-art models, we
demonstrate new modeling capabilities in CPath such as resolution-agnostic
tissue classification, slide classification using few-shot class prototypes,
and disease subtyping generalization in classifying up to 108 cancer types in
the OncoTree code classification system. UNI advances unsupervised
representation learning at scale in CPath in terms of both pretraining data and
downstream evaluation, enabling data-efficient AI models that can generalize
and transfer to a gamut of diagnostically-challenging tasks and clinical
workflows in anatomic pathology.
