标题： 基于图的MIL和干预训练在WSI分类器泛化中的作用 显示英文标题

标题： The Role of Graph-based MIL and Interventional Training in the Generalization of WSI Classifiers

摘要： 全切片成像（WSI），涉及病理切片的高分辨率数字扫描，已成为癌症诊断的金标准，但其千兆像素分辨率和标注数据集的稀缺性对深度学习模型构成了挑战。 多实例学习（MIL），一种广泛使用的弱监督方法，可以绕过对补丁级注释的需求。 然而，传统MIL方法忽略了补丁之间的空间关系，而这些关系对于癌症分级和诊断等任务至关重要。 为了解决这个问题，基于图的方法通过节点连接引入空间信息而受到关注。 尽管具有潜力，MIL和基于图的模型都容易学习虚假关联，例如WSI中的颜色变化，这会影响它们的鲁棒性。 在本论文中，我们对多种图构建技术、MIL模型、图-MIL方法和干预训练进行了广泛的比较，引入了一种新的框架，基于图的多实例学习与干预训练（GMIL-IT），用于WSI分类。 我们通过领域转移分析评估它们对模型泛化的影响，并证明仅基于图的模型实现了最初从干预训练中预期的泛化能力。 我们的代码在此处可用：github.com/ritamartinspereira/GMIL-IT 显示英文摘要

摘要： Whole Slide Imaging (WSI), which involves high-resolution digital scans of pathology slides, has become the gold standard for cancer diagnosis, but its gigapixel resolution and the scarcity of annotated datasets present challenges for deep learning models. Multiple Instance Learning (MIL), a widely-used weakly supervised approach, bypasses the need for patch-level annotations. However, conventional MIL methods overlook the spatial relationships between patches, which are crucial for tasks such as cancer grading and diagnosis. To address this, graph-based approaches have gained prominence by incorporating spatial information through node connections. Despite their potential, both MIL and graph-based models are vulnerable to learning spurious associations, like color variations in WSIs, affecting their robustness. In this dissertation, we conduct an extensive comparison of multiple graph construction techniques, MIL models, graph-MIL approaches, and interventional training, introducing a new framework, Graph-based Multiple Instance Learning with Interventional Training (GMIL-IT), for WSI classification. We evaluate their impact on model generalization through domain shift analysis and demonstrate that graph-based models alone achieve the generalization initially anticipated from interventional training. Our code is available here: github.com/ritamartinspereira/GMIL-IT