Multiscale Fourier transform multiple instance learning for the Gleason grading of prostate cancer from whole-slide images.

ABSTRACT:

Background

Prostate cancer is one of the most prevalent malignancies among men, and accurate diagnosis is crucial for effective treatment planning and prognostication. The Gleason grading system remains the gold standard for histopathological assessment, guiding both diagnosis and risk stratification. However, manual grading is time-consuming and subject to considerable inter-observer variability, which can lead to the misclassification of tumor aggressiveness. Such discrepancies may result in the overtreatment of indolent disease or the undertreatment of aggressive cancer, directly compromising clinical outcomes. Although computational pathology, particularly deep learning, offers a promising solution, its clinical adoption is often hindered by the need for large, pixel-level anno

SUBMITTER: Xie Z 

PROVIDER: S-EPMC12514604 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature