Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image
  Classification
Kunpeng Qiu Zhiying Zhou Yongxin Guo
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.