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

arXiv: https://arxiv.org/abs/2401.05771 · HTML: https://mlsanity.com/paper/2401.05771/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [Decoupled Contrastive Learning](https://mlsanity.com/paper/2110.06848/) (2021)
- [Dissecting Supervised Contrastive Learning](https://mlsanity.com/paper/2102.08817/) (2021)
- [Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks](https://mlsanity.com/paper/1809.03355/) (2018)
- [UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning](https://mlsanity.com/paper/2305.09299/) (2023)

## Built on by (most cited first, 6 of 6)

- [Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation](https://mlsanity.com/paper/2505.06068/) (2025)
- [Adaptively Distilled ControlNet: Accelerated Training and Superior Sampling for Medical Image Synthesis](https://mlsanity.com/paper/2507.23652/) (2025)
- [LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images](https://mlsanity.com/paper/2506.17983/) (2025)
- [Crucial-Diff: A Unified Diffusion Model for Crucial Image and Annotation Synthesis in Data-scarce Scenarios](https://mlsanity.com/paper/2507.09915/) (2025)
- [Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networks](https://mlsanity.com/paper/2602.02171/) (2026)
- [HOLA: Holistic Multi-Modal Alignment for Open-Set 3D Recognition](https://mlsanity.com/paper/2606.01334/) (2026)

## Abstract (arXiv)

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.