A new self-supervised model called UNI is introduced for computational pathology tasks. The model is pretrained using over 100 million images from diagnostic H&E-stained WSIs across 20 major tissue types. It outperforms previous models and demonstrates new capabilities in tissue classification and disease subtyping.
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications.
To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning 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 more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. 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 classification system
Computational Pathology Cpath Histopathological Entities Whole-Slide Images Wsis Pretrained Image Encoders Transfer Learning Self-Supervised Learning UNI Model Tissue Classification Disease Subtyping
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