TopoCL：面向医学影像的拓扑对比学习
TopoCL: Topological Contrastive Learning for Medical Imaging
将持续同调得到的拓扑特征（持久图）通过自注意力与交叉注意力编码，并与视觉表征用自适应MoE融合，为医学图像自监督预训练提供了一种可插拔的拓扑感知增强与表征融合方案。
原文依据 we propose a new topological CL framework (TopoCL) that explicitly exploits topological structures during contrastive learning for medical imaging
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论对比学习（CL）已成为从无标注图像中学习表征的强大方法。然而，现有CL方法主要关注视觉外观特征，而忽略了为医学图像分析提供有价值线索的拓扑特性（如连通模式、边界配置、空洞形成）。为解决这一局限，我们提出了一种新的拓扑对比学习框架（TopoCL），在医学影像的对比学习过程中显式利用拓扑结构。具体而言，我们首先引入拓扑感知增强，利用持久图之间的相对瓶颈距离控制拓扑扰动，在保留医学相关拓扑属性的同时实现可控的结构变化。然后，我们设计了一个分层拓扑编码器，通过自注意力和交叉注意力机制捕获拓扑特征。最后，我们开发了一个自适应混合专家（MoE）模块，动态整合视觉与拓扑表征。TopoCL可以无缝集成到现有CL方法中。我们在五种代表性CL方法（SimCLR、MoCo-v3、BYOL、DINO和Barlow Twins）和五个多样化的医学图像分类数据集上评估了TopoCL。实验结果表明，TopoCL取得了持续改进：线性探测分类准确率平均提升3.26%，具有强统计显著性，验证了其有效性。
英文原文摘要 1500 字符
Contrastive learning (CL) has become a powerful approach for learning representations from unlabeled images. However, existing CL methods focus predominantly on visual appearance features while neglecting topological characteristics (e.g., connectivity patterns, boundary configurations, cavity formations) that provide valuable cues for medical image analysis. To address this limitation, we propose a new topological CL framework (TopoCL) that explicitly exploits topological structures during contrastive learning for medical imaging. Specifically, we first introduce topology-aware augmentations that control topological perturbations using a relative bottleneck distance between persistence diagrams, preserving medically relevant topological properties while enabling controlled structural variations. We then design a Hierarchical Topology Encoder that captures topological features through self-attention and cross-attention mechanisms. Finally, we develop an adaptive mixture-of-experts (MoE) module to dynamically integrate visual and topological representations. TopoCL can be seamlessly integrated with existing CL methods. We evaluate TopoCL on five representative CL methods (SimCLR, MoCo-v3, BYOL, DINO, and Barlow Twins) and five diverse medical image classification datasets. The experimental results show that TopoCL achieves consistent improvements: an average gain of 3.26% in linear probe classification accuracy with strong statistical significance, verifying its effectiveness.