PSA-MIL: 一种基于概率空间注意力的多实例学习方法用于整张滑动图像分类
PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classification
AI总结：
PSA-MIL通过整合空间上下文的注意力机制，提升整张滑动图像分类的准确性和效率，同时减少计算成本。
摘要：
整张滑动图像（WSI）是高分辨率数字扫描，广泛用于医学诊断。WSI分类通常采用多实例学习（MIL），其中将滑片分割成小块并作为相互关联的实例进行处理。尽管基于注意力的MIL方法旨在识别最有信息量的小块，但它们往往无法充分利用其中的空间关系，可能忽略对准确诊断至关重要的复杂组织结构。为了解决这一限制，我们提出了概率空间注意力MIL（PSA-MIL），一种新颖的基于注意力的MIL框架，通过可学习的距离衰减先验将空间上下文整合到注意力机制中，以概率解释的自注意力作为后验分布的形式进行公式化。这种公式化使在训练过程中能够动态推断空间关系，消除了先前方法中通常施加的预定义假设。此外，我们建议一种空间修剪策略用于后验，有效减少了自注意力的二次复杂度。为了进一步增强空间建模，我们引入了一种多样性损失，鼓励注意力头之间的变化，确保每个头都能捕捉到不同的空间表示。共同地，PSA-MIL实现了更数据驱动和适应性的空间上下文整合，超越了预定义的约束。我们在上下文和非上下文基线中均取得了最先进的性能，同时显著降低了计算成本。
英文摘要：
Whole Slide Images (WSIs) are high-resolution digital scans widely used in medical diagnostics. WSI classification is typically approached using Multiple Instance Learning (MIL), where the slide is partitioned into tiles treated as interconnected instances. While attention-based MIL methods aim to identify the most informative tiles, they often fail to fully exploit the spatial relationships among them, potentially overlooking intricate tissue structures crucial for accurate diagnosis. To address this limitation, we propose Probabilistic Spatial Attention MIL (PSA-MIL), a novel attention-based MIL framework that integrates spatial context into the attention mechanism through learnable distance-decayed priors, formulated within a probabilistic interpretation of self-attention as a posterior distribution. This formulation enables a dynamic inference of spatial relationships during training, eliminating the need for predefined assumptions often imposed by previous approaches. Additionally, we suggest a spatial pruning strategy for the posterior, effectively reducing self-attention's quadratic complexity. To further enhance spatial modeling, we introduce a diversity loss that encourages variation among attention heads, ensuring each captures distinct spatial representations. Together, PSA-MIL enables a more data-driven and adaptive integration of spatial context, moving beyond predefined constraints. We achieve state-of-the-art performance across both contextual and non-contextual baselines, while significantly reducing computational costs.
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。
