EvoPS：计算病理学中全切片图像分析的进化补丁选择
EvoPS: Evolutionary Patch Selection for Whole Slide Image Analysis in Computational Pathology
AI总结：
针对计算病理学中WSI补丁选择的效率与准确性权衡问题，提出EvoPS多目标进化框架，在TCGA四个癌症队列验证中减少90%以上训练补丁并保持或提升F1分数，优化WSI表示的成本与性能平衡。
摘要：
在计算病理学中，全切片图像（WSIs）的十亿像素规模使其必须被分割成数千个较小的补丁。分析这些高维补丁嵌入在计算上成本高昂，并且存在许多无信息补丁稀释关键诊断信号的风险。现有补丁选择方法通常依赖随机采样或简单聚类启发式，且往往无法明确管理所选补丁数量与所得切片表示准确性之间的关键权衡。为解决这一差距，我们提出EvoPS（进化补丁选择），这是一个将补丁选择表述为多目标优化问题的新颖框架，利用进化搜索同时最小化所选补丁嵌入的数量并最大化下游相似性搜索任务的性能，生成最优权衡解决方案的帕累托前沿。我们使用五个预训练深度学习模型（包括监督式CNNs和大型自监督基础模型）生成补丁嵌入，在来自癌症基因组图谱（TCGA）的四个主要癌症队列上验证了我们的框架。结果表明，与使用通过标准提取管道选择的所有可用补丁嵌入的基线相比，EvoPS可将所需的训练补丁嵌入数量减少90%以上，同时持续保持甚至提高最终分类F1分数。EvoPS框架为创建高效、准确且可解释的WSI表示提供了一种稳健且有原则的方法，使用户能够在计算成本与诊断性能之间选择最优平衡。
英文摘要：
In computational pathology, the gigapixel scale of Whole-Slide Images (WSIs) necessitates their division into thousands of smaller patches. Analyzing these high-dimensional patch embeddings is computationally expensive and risks diluting key diagnostic signals with many uninformative patches. Existing patch selection methods often rely on random sampling or simple clustering heuristics and typically fail to explicitly manage the crucial trade-off between the number of selected patches and the accuracy of the resulting slide representation. To address this gap, we propose EvoPS (Evolutionary Patch Selection), a novel framework that formulates patch selection as a multi-objective optimization problem and leverages an evolutionary search to simultaneously minimize the number of selected patch embeddings and maximize the performance of a downstream similarity search task, generating a Pareto front of optimal trade-off solutions. We validated our framework across four major cancer cohorts from The Cancer Genome Atlas (TCGA) using five pretrained deep learning models to generate patch embeddings, including both supervised CNNs and large self-supervised foundation models. The results demonstrate that EvoPS can reduce the required number of training patch embeddings by over 90% while consistently maintaining or even improving the final classification F1-score compared to a baseline that uses all available patches' embeddings selected through a standard extraction pipeline. The EvoPS framework provides a robust and principled method for creating efficient, accurate, and interpretable WSI representations, empowering users to select an optimal balance between computational cost and diagnostic performance.
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。
