收录解读
数字病理学中，从全切片图像预测生物标志物面临计算效率低下的问题，现有方法处理大量冗余图块并需要复杂聚合模型。
本文提出EAGLE框架，模仿病理学家选择性分析信息区域，结合任务无关的图块选择与详细特征提取。
在涵盖9种癌症类型的43项任务上，EAGLE相比图块聚合方法性能提升高达23%，每张切片处理仅需2.27秒，计算时间减少超过99%，并进行了系统消融和注意力分析验证。
EAGLE的高效性和可审计性使其适用于快速临床工作流和多组学整合，但方法原创性有限且通用性局限于病理领域。
原始摘要
Abstract Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature extraction and is benchmarked against leading slide- and tile-level foundation models across 43 tasks from nine cancer types spanning morphology, biomarker prediction, treatment response and prognosis. EAGLE outperforms patch aggregation methods by up to 23% and achieves the highest overall classification performance. It processes one slide in 2.27 s, reducing computational time by more than 99% compared with existing models. This efficiency supports rapid and auditable workflows by enabling review of the exact tiles used for each prediction and reducing dependence on high-performance computing. By reliably identifying informative regions and minimizing artifacts, EAGLE provides robust and auditable outputs, supported by systematic negative controls and attention concentration analyses. Its unified embedding enables rapid slide search, integration into multi-omics pipelines and emerging clinical foundation models.