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.

翻译：无线胶囊内窥镜(WCE)图像中的精确病变分类对于胃肠道(GI)癌症的早期诊断和治疗至关重要。然而，该任务面临微小病灶和背景干扰等挑战。此外，WCE图像表现出更高的类内方差和类间相似性，增加了问题的复杂性。为应对这些挑战，我们提出了一种用于WCE图像分类的解耦监督对比学习方法，从显著性增强器生成的放大WCE图像中学习鲁棒表征。具体而言，我们将均匀下采样的WCE图像作为锚点，将同一类别的WCE图像（尤其是其放大图像）作为正样本。该方法通过解耦监督对比学习，使特征提取器能够从同一图像的不同视角捕获丰富表征。基于这些表征训练线性分类器，在10个训练周期内实现了92.01%的整体准确率，在两个公开WCE数据集混合测试集上以0.72%的绝对优势超越了先前最先进方法(SOTA)。代码开源地址：https://github.com/Qiukunpeng/DSCL。