arXiv 2311.000482024-08-01cs.CVcs.AIcs.LG

SC-MIL：用于全切片图像分类的稀疏编码多实例学习

SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification

Mallinckrodt Institute of Radiology, Washington University School of Medicine（华盛顿大学医学院马林克罗特放射学研究所）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine（华盛顿大学医学院信息学、数据科学与生物统计学研究所）

Peijie Qiu, Pan Xiao, Wenhui Zhu, Yalin Wang, Aristeidis Sotiras

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AI总结：

本文提出SC-MIL方法，通过深度展开稀疏字典学习同时优化实例特征嵌入与相关性建模，以即插即用模块显著提升现有MIL方法在全切片图像分类中的性能。

摘要：

多实例学习（MIL）已被广泛用于弱监督全切片图像（WSI）分类。典型的MIL方法包括一个特征嵌入部分，该部分通过预训练的特征提取器将实例嵌入为特征，以及一个将实例嵌入组合为预测的MIL聚合器。大多数工作通常集中在改进这些部分上。这涉及通过自监督预训练来细化特征嵌入，以及单独建模实例之间的相关性。在本文中，我们提出了一种稀疏编码MIL（SC-MIL）方法，通过利用稀疏字典学习同时解决这两个方面。稀疏字典学习通过将实例表示为过完备字典中原子的稀疏线性组合来捕获实例之间的相似性。此外，施加稀疏性通过抑制不相关实例同时保留最相关实例来改进实例特征嵌入。为了使传统的稀疏编码算法与深度学习兼容，我们利用深度展开将其展开为一个稀疏编码模块。所提出的SC模块可以以即插即用的方式并入任何现有的MIL框架中，且计算成本可接受。在多个数据集上的实验结果表明，所提出的SC模块能够显著提升最先进MIL方法的性能。代码可在 https://github.com/sotiraslab/SCMIL.git 获取。

英文摘要：

Multiple Instance Learning (MIL) has been widely used in weakly supervised whole slide image (WSI) classification. Typical MIL methods include a feature embedding part, which embeds the instances into features via a pre-trained feature extractor, and an MIL aggregator that combines instance embeddings into predictions. Most efforts have typically focused on improving these parts. This involves refining the feature embeddings through self-supervised pre-training as well as modeling the correlations between instances separately. In this paper, we proposed a sparsely coding MIL (SC-MIL) method that addresses those two aspects at the same time by leveraging sparse dictionary learning. The sparse dictionary learning captures the similarities of instances by expressing them as sparse linear combinations of atoms in an over-complete dictionary. In addition, imposing sparsity improves instance feature embeddings by suppressing irrelevant instances while retaining the most relevant ones. To make the conventional sparse coding algorithm compatible with deep learning, we unrolled it into a sparsely coded module leveraging deep unrolling. The proposed SC module can be incorporated into any existing MIL framework in a plug-and-play manner with an acceptable computational cost. The experimental results on multiple datasets demonstrated that the proposed SC module could substantially boost the performance of state-of-the-art MIL methods. The codes are available at \href{https://github.com/sotiraslab/SCMIL.git}{https://github.com/sotiraslab/SCMIL.git}.

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。