用拓扑分析技术实现无需训练的病理图像快速检索
THIR: Topological Histopathological Image Retrieval
- 基于立方体持续同调提取图像拓扑特征，生成可解释的指纹向量
- 在BreaKHis数据集上超越主流有监督与无监督方法，20分钟内完成全库检索
- 适合临床快速辅助诊断，尤其适用于缺乏标注数据的场景
世界卫生组织数据显示，2020年乳腺癌导致约68.5万名女性死亡。早期诊断与准确临床决策对减轻这一全球负担至关重要。本文提出THIR，一种新型基于内容的医学图像检索框架，专门利用拓扑数据分析中的贝蒂数（Betti numbers）来刻画和检索组织病理图像的内在结构模式。与依赖大量训练数据、人工标注及强大GPU资源的传统深度学习方法不同，THIR完全无需监督。它通过立方体持续同调直接从RGB病理图像中提取拓扑指纹，编码环结构的演化过程，生成紧凑且可解释的特征向量。相似性检索通过计算这些拓扑描述子之间的距离实现，高效返回前K个最相关匹配。在BreaKHis数据集上的大量实验表明，THIR性能优于当前最先进的有监督与无监督方法。该系统在标准CPU上处理整个数据集耗时不足20分钟，提供了一种快速、可扩展、无需训练的临床图像检索解决方案。
原文摘要 · Abstract (English)
According to the World Health Organization, breast cancer claimed the lives of approximately 685,000 women in 2020. Early diagnosis and accurate clinical decision making are critical in reducing this global burden. In this study, we propose THIR, a novel Content-Based Medical Image Retrieval (CBMIR) framework that leverages topological data analysis specifically, Betti numbers derived from persistent homology to characterize and retrieve histopathological images based on their intrinsic structural patterns. Unlike conventional deep learning approaches that rely on extensive training, annotated datasets, and powerful GPU resources, THIR operates entirely without supervision. It extracts topological fingerprints directly from RGB histopathological images using cubical persistence, encoding the evolution of loops as compact, interpretable feature vectors. The similarity retrieval is then performed by computing the distances between these topological descriptors, efficiently returning the top-K most relevant matches. Extensive experiments on the BreaKHis dataset demonstrate that THIR outperforms state of the art supervised and unsupervised methods. It processes the entire dataset in under 20 minutes on a standard CPU, offering a fast, scalable, and training free solution for clinical image retrieval.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。