THIR：拓扑组织病理学图像检索
THIR: Topological Histopathological Image Retrieval
AI总结：
针对乳腺癌早期诊断需求，提出无监督拓扑组织病理学图像检索框架THIR，利用持久同调提取拓扑指纹，在BreaKHis数据集上优于现有方法且CPU处理全数据集仅需20分钟内。
摘要：
根据世界卫生组织的数据，2020年乳腺癌夺去了约685000名女性的生命。早期诊断和准确的临床决策对于减轻这一全球负担至关重要。在本研究中，我们提出THIR，一种新颖的基于内容的医学图像检索（CBMIR）框架，该框架利用拓扑数据分析——具体而言是源自持久同调的贝蒂数——来基于其内在结构模式表征和检索组织病理学图像。与依赖大量训练、标注数据集和强大GPU资源的传统深度学习方法不同，THIR完全在无监督条件下运行。它使用立方持久性直接从RGB组织病理学图像中提取拓扑指纹，将环路的演化编码为紧凑、可解释的特征向量。然后通过计算这些拓扑描述符之间的距离进行相似性检索，高效返回前K个最相关的匹配项。在BreaKHis数据集上的大量实验表明，THIR优于最先进的有监督和无监督方法。它在标准CPU上处理整个数据集不到20分钟，为临床图像检索提供了一种快速、可扩展且无需训练的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
