Exploiting Label-Independent Regularization from Spatial Dependencies for Whole Slide Image Analysis
This addresses the challenge of precise disease diagnosis from gigapixel-scale tissue images with scarce annotations, representing an incremental advance in medical image analysis.
The paper tackles the problem of analyzing whole slide images with limited annotations by proposing a spatially regularized multiple instance learning framework that leverages spatial relationships as regularization signals, achieving significant improvements over state-of-the-art methods on multiple public datasets.
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.