整张滑片图像分类中的Jigsaw正则化
Jigsaw Regularization in Whole-Slide Image Classification
AI总结：
本文提出一种基于图神经网络和Jigsaw正则化的新型方法，通过引入视觉基础模型嵌入和跨块空间意识，提升多实例学习在乳腺癌、头颈癌和结肠癌等基准数据集上的分类性能。
摘要：
计算病理学涉及将染色组织数字化为包含数十亿像素的整张滑片图像（WSIs），这些像素以连续块排列。WSI的统计分析大多集中在通过多实例学习（MIL）进行分类，其中滑片级标签是从未标记的块推断而来的。大多数MIL方法将块视为可交换的，忽略了肿瘤图像下丰富的空间和拓扑结构。本文基于近期基于图的方法，旨在将空间意识融入MIL。我们的方法有两点创新：（1）我们部署视觉基础模型嵌入以在每个块内整合局部空间结构；（2）利用图神经网络和新的Jigsaw正则化实现跨块空间意识。我们发现，这两种特征的结合显著提升了在乳腺癌、头颈癌和结肠癌基准数据集上的分类性能，优于最先进的基于注意力的MIL方法。
英文摘要：
Computational pathology involves the digitization of stained tissues into whole-slide images (WSIs) that contain billions of pixels arranged as contiguous patches. Statistical analysis of WSIs largely focuses on classification via multiple instance learning (MIL), in which slide-level labels are inferred from unlabeled patches. Most MIL methods treat patches as exchangeable, overlooking the rich spatial and topological structure that underlies tissue images. This work builds on recent graph-based methods that aim to incorporate spatial awareness into MIL. Our approach is new in two regards: (1) we deploy vision \emph{foundation-model embeddings} to incorporate local spatial structure within each patch, and (2) achieve across-patch spatial awareness using graph neural networks together with a novel {\em jigsaw regularization}. We find that a combination of these two features markedly improves classification over state-of-the-art attention-based MIL approaches on benchmark datasets in breast, head-and-neck, and colon cancer.
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。
