CLEAR-WSI：面向病理全切片图像检索的诊断驱动新框架
Towards Scalable Diagnostic-Driven Whole-Slide Image Retrieval for Pathology Archives
一篇病理AI检索论文，CLEAR-WSI用固定长度嵌入在四个数据集上都超过最强基线，做数字病理检索的可以看看具体方法。
arXiv 论文提出 CLEAR-WSI（Constant-Length Embedding for Automatic Retrieval of Whole-Slide Images），一个可扩展的病理全切片图像检索框架。它用固定长度的切片嵌入表示，并依据预测诊断标签或真实标签对检索候选进行精炼，新病例可增量加入记忆库，让检索档案随时间增长。在两个公开数据集和一个私有临床数据集、四类检索任务的基准上，CLEAR-WSI 的 Precision@5 相比最强基线在 CAMELYON16 提升 3.10、BRACS-1 提升 2.75、BRACS-2 提升 1.95、X-HER2 提升 3.75。消融和延迟实验进一步分析了各组件贡献及其部署可行性。
Towards Scalable Diagnostic-Driven Whole-Slide Image Retrieval for Pathology Archives
The growing adoption of digital pathology has rapidly expanded collections of hematoxylin-and-eosin whole-slide images, yet efficient navigation through these archives remains challenging, as metadata is often inconsistent or unavailable. Content-based image retrieval addresses this challenge by identifying similar cases directly from visual representations instead of manual annotations or metadata. Existing retrieval systems have demonstrated promising performance but largely rely on handcrafted indexing pipelines and predefined sampling strategies, constraining scalability and generalization. Here, we present CLEAR-WSI (Constant-Length Embedding for Automatic Retrieval of Whole-Slide Images) as a scalable, diagnostic-driven slide-retrieval framework, validated on both public and clinical data. Using a fixed-length slide embedding, CLEAR-WSI refines retrieved candidates with either a predicted diagnostic label or the ground-truth label, depending on the retrieval setting. Newly processed cases are subsequently added to the expanding memory bank, allowing the retrieval archive to grow over time. On a comprehensive benchmark consisting of two public and one private clinical datasets representing four retrieval tasks, CLEAR-WSI improves Precision@5 over the strongest evaluated baseline, with gains on CAMELYON16 (3.10), BRACS-1 (2.75), BRACS-2 (1.95), and X-HER2 (3.75). Additional ablation and latency studies further examine the contribution of each component and highlight its potential for scalable deployment.