论文标题
DGMIL：分布指导整个幻灯片图像分类的多个实例学习
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification
论文作者
论文摘要
多个实例学习（MIL）广泛用于分析组织病理学全幻灯片图像（WSIS）。但是，现有的MIL方法不会明确地对数据分布进行建模，而仅通过训练分类器来歧视行李级或实例级决策边界。在本文中，我们提出了DGMIL：一种用于WSI分类和阳性贴剂定位的深层MIL框架的特征分布。我们没有设计复杂的判别网络体系结构，而是揭示组织病理学图像数据的固有特征分布可以作为分类的非常有效的指南。我们提出了一种集群条件的特征分布建模方法和基于伪标签的迭代特征空间改进策略，以便在最终特征空间中可以轻松分离正面和负面实例。 CamelyOn16数据集和TCGA肺癌数据集的实验表明，我们的方法可以实现全球分类和正面贴片定位任务的新SOTA。
Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of designing complex discriminative network architectures, we reveal that the inherent feature distribution of histopathological image data can serve as a very effective guide for instance classification. We propose a cluster-conditioned feature distribution modeling method and a pseudo label-based iterative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.
