Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images.

ABSTRACT:

Background

Accurate gastrointestinal (GI) lesion segmentation is crucial in diagnosing digestive tract diseases. An automatic lesion segmentation in endoscopic images is vital to relieving physicians' burden and improving the survival rate of patients. However, pixel-wise annotations are highly intensive, especially in clinical settings, while numerous unlabeled image datasets could be available, although the significant results of deep learning approaches in several tasks heavily depend on large labeled datasets. Limited labeled data also hinder trained models' generalizability under fully supervised learning for computer-aided diagnosis (CAD) systems.

Methods

This work proposes a generative adversarial learning-based semi-supervised segmentation framework for GI lesion dia

SUBMITTER: Lonseko ZM 

PROVIDER: S-EPMC9864320 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature