A foundation model for clinical-grade computational pathology and rare cancers detection.

ABSTRACT: The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade mod

SUBMITTER: Vorontsov E 

PROVIDER: S-EPMC11485232 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature