Exploiting Label-Independent Regularization from Spatial Dependencies for Whole Slide Image Analysis

TL;DR

This paper introduces a spatially regularized multiple instance learning framework for whole slide image analysis, effectively leveraging spatial relationships among patches to improve disease diagnosis accuracy despite limited annotations.

Contribution

It proposes a novel regularization method that exploits spatial dependencies in tissue images, enhancing feature learning without requiring additional labels.

Findings

Significant performance improvements over existing methods.

Effective utilization of spatial relationships improves patch discrimination.

Robustness to limited annotations and data variability.

Abstract

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.