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Accurate lesion classification in Wireless Capsule Endoscopy (WCE) images isvital for early diagnosis and treatment of gastrointestinal (GI) cancers.However, this task is confronted with challenges like tiny lesions andbackground interference. Additionally, WCE images exhibit higher intra-classvariance and inter-class similarities, adding complexity. To tackle thesechallenges, we propose Decoupled Supervised Contrastive Learning for WCE imageclassification, learning robust representations from zoomed-in WCE imagesgenerated by Saliency Augmentor. Specifically, We use uniformly down-sampledWCE images as anchors and WCE images from the same class, especially theirzoomed-in images, as positives. This approach empowers the Feature Extractor tocapture rich representations from various views of the same image, facilitatedby Decoupled Supervised Contrastive Learning. Training a linear Classifier onthese representations within 10 epochs yields an impressive 92.01% overallaccuracy, surpassing the prior state-of-the-art (SOTA) by 0.72% on a blend oftwo publicly accessible WCE datasets. Code is available at:https://github.com/Qiukunpeng/DSCL.

Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image Classification