Classification of endoscopic image and video frames using distance metric-based learning with interpolated latent features.

ABSTRACT: Conventional Endoscopy (CE) and Wireless Capsule Endoscopy (WCE) are well known tools for diagnosing gastrointestinal (GI) tract related disorders. Defining the anatomical location within the GI tract helps clinicians determine appropriate treatment options, which can reduce the need for repetitive endoscopy. Limited research addresses the localization of the anatomical location of WCE and CE images using classification, mainly due to the difficulty in collecting annotated data. In this study, we present a few-shot learning method based on distance metric learning which combines transfer-learning and manifold mixup schemes to localize and classify endoscopic images and video frames. The proposed method allows us to develop a pipeline for endoscopy video sequence localization that can be tr

SUBMITTER: Sedighipour Chafjiri F 

PROVIDER: S-EPMC10020761 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature