A deep learning framework to iDentify prOgnostically releVant cancEr Regions (DOVER) within whole slide histopathology images.

ABSTRACT: The recent advancements in computational pathology focus on extracting valuable prognostic insights from whole-slide images (WSIs). These methods primarily involve deep learning-based or handcrafted feature representations of the disease's morphologic patterns associated with outcomes. However, determining the most prognostic regions within tumors remains challenging due to significant morphologic heterogeneity even within manually annotated tumor areas. In other words, the question is not simply what type of representation is appropriate to predict cancer outcomes, but specifically where to mine those representations. To address this issue, a deep learning framework to identify prognostically relevant (PR) cancer regions (DOVER) within WSIs is presented. DOVER leverages patterns mined fro

SUBMITTER: Wang X 

PROVIDER: S-EPMC12640559 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature