Towards a general-purpose foundation model for computational pathology.

ABSTRACT: 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 tha

SUBMITTER: Chen RJ 

PROVIDER: S-EPMC11403354 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature