Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image Classification
This work addresses early diagnosis of gastrointestinal cancers by improving WCE image classification, but it is incremental as it builds on existing contrastive learning methods with a modest accuracy gain.
The paper tackles lesion classification in Wireless Capsule Endoscopy (WCE) images by proposing Decoupled Supervised Contrastive Learning, which uses zoomed-in images to learn robust representations, achieving 92.01% overall accuracy and surpassing prior state-of-the-art by 0.72%.
Accurate lesion classification in Wireless Capsule Endoscopy (WCE) images is vital for early diagnosis and treatment of gastrointestinal (GI) cancers. However, this task is confronted with challenges like tiny lesions and background interference. Additionally, WCE images exhibit higher intra-class variance and inter-class similarities, adding complexity. To tackle these challenges, we propose Decoupled Supervised Contrastive Learning for WCE image classification, learning robust representations from zoomed-in WCE images generated by Saliency Augmentor. Specifically, We use uniformly down-sampled WCE images as anchors and WCE images from the same class, especially their zoomed-in images, as positives. This approach empowers the Feature Extractor to capture rich representations from various views of the same image, facilitated by Decoupled Supervised Contrastive Learning. Training a linear Classifier on these representations within 10 epochs yields an impressive 92.01% overall accuracy, surpassing the prior state-of-the-art (SOTA) by 0.72% on a blend of two publicly accessible WCE datasets. Code is available at: https://github.com/Qiukunpeng/DSCL.