用于组织病理学全切片图像分析的带掩码去噪机制的智能体聚合器
Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
AI总结：
提出AMD-MIL，一种带掩码去噪机制的智能体聚合器，用于弱监督全切片图像分类，通过智能体令牌和动态掩码去噪改进注意力分配，在多个数据集上超越现有方法。
摘要：
组织病理学分析是医学诊断的金标准。全切片图像（WSIs）的准确分类和感兴趣区域（ROIs）定位可以辅助病理学家进行诊断。WSI的十亿像素分辨率和缺乏细粒度标注使得直接分类和分析具有挑战性。在弱监督学习中，多实例学习（MIL）为WSI分类提供了一种有前景的方法。主流策略是使用注意力机制来衡量实例重要性以进行分类。然而，注意力机制无法捕获实例间信息，且自注意力导致二次方计算复杂度。为解决这些挑战，我们提出了AMD-MIL，一种带掩码去噪机制的智能体聚合器。智能体令牌作为查询和键之间的中间变量来计算实例重要性。从智能体聚合值映射的掩码和去噪矩阵，动态地掩蔽低贡献表示并消除噪声。AMD-MIL通过调整特征表示实现更好的注意力分配，捕获癌症中的微转移，并提高可解释性。在CAMELYON-16、CAMELYON-17、TCGA-KIDNEY和TCGA-LUNG上的大量实验表明，AMD-MIL优于最先进的方法。
英文摘要：
Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist pathologists in diagnosis. The gigapixel resolution of WSI and the absence of fine-grained annotations make direct classification and analysis challenging. In weakly supervised learning, multiple instance learning (MIL) presents a promising approach for WSI classification. The prevailing strategy is to use attention mechanisms to measure instance importance for classification. However, attention mechanisms fail to capture inter-instance information, and self-attention causes quadratic computational complexity. To address these challenges, we propose AMD-MIL, an agent aggregator with a mask denoise mechanism. The agent token acts as an intermediate variable between the query and key for computing instance importance. Mask and denoising matrices, mapped from agents-aggregated value, dynamically mask low-contribution representations and eliminate noise. AMD-MIL achieves better attention allocation by adjusting feature representations, capturing micro-metastases in cancer, and improving interpretability. Extensive experiments on CAMELYON-16, CAMELYON-17, TCGA-KIDNEY, and TCGA-LUNG show AMD-MIL's superiority over state-of-the-art methods.
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。
