WSD-MIL：窗口尺度衰减多实例学习用于整张滑动图像分类
WSD-MIL: Window Scale Decay Multiple Instance Learning for Whole Slide Image Classification
AI总结：
WSD-MIL通过引入窗口尺度衰减机制，提升整张滑动图像分类中对不同尺度肿瘤区域的建模能力，同时提高计算效率。
摘要：
近年来，预训练基础模型与多实例学习（MIL）的结合已提高了计算病理学的诊断准确性。然而，现有MIL方法侧重于优化特征提取器和聚合策略，而忽略了整张滑动图像（WSI）中实例之间的复杂语义关系。尽管基于Transformer的MIL方法旨在建模实例依赖性，但二次计算复杂性限制了其扩展性到大规模WSI。此外，由于不同WSI中肿瘤区域尺度的变化显著，现有基于Transformer的方法采用固定尺度注意力机制在精确捕捉局部实例相关性方面面临重大挑战，并未能考虑基于距离的衰减效应。为了解决这些挑战，我们提出了窗口尺度衰减MIL（WSD-MIL），旨在增强对不同尺度肿瘤区域的建模能力，同时提高计算效率。WSD-MIL包括：1）基于窗口尺度衰减的注意力模块，该模块采用基于聚类的采样策略以减少计算成本，同时逐步衰减注意力窗口尺度，以在不同尺度上捕捉局部实例关系；2）基于挤压和激励的区域门模块，该模块动态调整窗口权重以增强全局信息建模。实验结果表明，WSD-MIL在CAMELYON16和TCGA-BRCA数据集上实现了最先进的性能，同时将计算内存减少了62%。代码将公开发布。
英文摘要：
In recent years, the integration of pre-trained foundational models with multiple instance learning (MIL) has improved diagnostic accuracy in computational pathology. However, existing MIL methods focus on optimizing feature extractors and aggregation strategies while overlooking the complex semantic relationships among instances within whole slide image (WSI). Although Transformer-based MIL approaches aiming to model instance dependencies, the quadratic computational complexity limits their scalability to large-scale WSIs. Moreover, due to the pronounced variations in tumor region scales across different WSIs, existing Transformer-based methods employing fixed-scale attention mechanisms face significant challenges in precisely capturing local instance correlations and fail to account for the distance-based decay effect of patch relevance. To address these challenges, we propose window scale decay MIL (WSD-MIL), designed to enhance the capacity to model tumor regions of varying scales while improving computational efficiency. WSD-MIL comprises: 1) a window scale decay based attention module, which employs a cluster-based sampling strategy to reduce computational costs while progressively decaying attention window-scale to capture local instance relationships at varying scales; and 2) a squeeze-and-excitation based region gate module, which dynamically adjusts window weights to enhance global information modeling. Experimental results demonstrate that WSD-MIL achieves state-of-the-art performance on the CAMELYON16 and TCGA-BRCA datasets while reducing 62% of the computational memory. The code will be publicly available.
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。
