The segmentation of endoscopic images plays a vital role in computer-aided diagnosis and treatment. The advancements in deep learning have led to the employment of numerous models for endoscopic tumor segmentation, achieving promising segmentation performance. Despite recent advancements, precise segmentation remains challenging due to limited annotations and the issue of low contrast. To address these issues, we propose a novel semi-supervised segmentation framework termed LoCo via low-contrast-enhanced contrastive learning (LCC). This innovative approach effectively harnesses the vast amounts of unlabeled data available for endoscopic image segmentation, improving both accuracy and robustness in the segmentation process. Specifically, LCC incorporates two advanced strategies to enhance the distinctiveness of low-contrast pixels: inter-class contrast enhancement (ICE) and boundary contrast enhancement (BCE), enabling models to segment low-contrast pixels among malignant tumors, benign tumors, and normal tissues. Additionally, a confidence-based dynamic filter (CDF) is designed for pseudo-label selection, enhancing the utilization of generated pseudo-labels for unlabeled data with a specific focus on minority classes. Extensive experiments conducted on two public datasets, as well as a large proprietary dataset collected over three years, demonstrate that LoCo achieves state-of-the-art results, significantly outperforming previous methods. The source code of LoCo is available at the URL of https://github.com/AnoK3111/LoCo.

翻译：内窥镜图像分割在计算机辅助诊断与治疗中发挥着关键作用。深度学习技术的进步促使众多模型被应用于内窥镜肿瘤分割，并取得了显著的分割性能。尽管近期研究取得了进展，但由于标注数据有限及低对比度问题，精确分割仍面临挑战。为解决这些问题，本文提出一种新颖的半监督分割框架LoCo，其通过低对比度增强对比学习（LCC）机制实现。该创新方法有效利用了内窥镜图像分割中大量可用的未标注数据，提升了分割过程的准确性与鲁棒性。具体而言，LCC融合了两种先进策略以增强低对比度像素的区分度：类间对比度增强（ICE）与边界对比度增强（BCE），使模型能够有效分割恶性肿瘤、良性肿瘤与正常组织间的低对比度像素。此外，本文设计了基于置信度的动态滤波器（CDF）进行伪标签筛选，重点提升对少数类别的关注，从而优化未标注数据生成伪标签的利用率。在两个公开数据集及一个历时三年收集的大型私有数据集上的大量实验表明，LoCo取得了最先进的性能，显著优于现有方法。LoCo的源代码公开于https://github.com/AnoK3111/LoCo。