利用组织切片的局部空间关系增强弱监督学习效果
Exploiting Label-Independent Regularization from Spatial Dependencies for Whole Slide Image Analysis
- 通过像素块间空间结构关系构建无标签正则化信号
- 在多个公开数据集上优于当前最优方法，提升诊断准确率
- 适合医学图像分析、弱监督学习研究者参考
全切片图像（Whole Slide Images）以吉比特级分辨率呈现组织样本，对精准疾病诊断至关重要。但其分析受限于数据规模庞大和标注稀疏。现有基于包的学习（MIL）方法面临根本性不平衡：单一整体标签需指导大量局部切片特征的学习。这种稀疏监督使训练中难以可靠识别判别性切片，导致优化不稳定、性能不佳。本文提出一种空间正则化MIL框架，利用切片特征间的内在空间关系作为无标签正则化信号。通过联合优化特征驱动的空间重建与标签引导的分类目标，强制保持内在结构模式与监督信号的一致性。在多个公开数据集上的实验表明，该方法显著优于当前最优方法，为弱监督病理图像分析提供了新方向。
原文摘要 · Abstract (English)
Whole slide images, with their gigapixel-scale panoramas of tissue samples, are pivotal for precise disease diagnosis. However, their analysis is hindered by immense data size and scarce annotations. Existing MIL methods face challenges due to the fundamental imbalance where a single bag-level label must guide the learning of numerous patch-level features. This sparse supervision makes it difficult to reliably identify discriminative patches during training, leading to unstable optimization and suboptimal solutions. We propose a spatially regularized MIL framework that leverages inherent spatial relationships among patch features as label-independent regularization signals. Our approach learns a shared representation space by jointly optimizing feature-induced spatial reconstruction and label-guided classification objectives, enforcing consistency between intrinsic structural patterns and supervisory signals. Experimental results on multiple public datasets demonstrate significant improvements over state-of-the-art methods, offering a promising direction.
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