• Propose MorphoNet framework to capture morphological patterns and long-range tissue structures in WSIs. • Develop a Morphological Sub-Region Grouping mechanism to model WSIs as spatially coherent sub-regions. • Introduce a spatial-aware clustering approach and a sub-region aggregation strategy to derive sub-region embeddings. • Achieve superior performance across 10 public benchmarks, outperforming state-of-the-art methods in tumor subtyping and survival prediction. Representation learning of Whole slide image (WSI) is fundamental to computational pathology, enabling tasks such as tumor subtyping, survival prediction, and cancer grading. Existing methods typically tile WSIs into thousands of small patches and aggregate patch features into slide-level embeddings, but this patch-centric paradigm suffers from redundancy and suboptimal spatial modeling. Built upon these patch-level embeddings, Multiple Instance Learning (MIL) methods overfit to scattered discriminative patches, graph-based models mainly capture local neighborhoods, and prototype-based approaches often ignore spatial coherence and under-represent rare tissue patterns. To address these challenges, we propose MorphoNet, a Morph ological structure learning Net work that captures long-range spatial tissue relationships while extracting informative morphological patterns. The key idea of MorphoNet is Morphological Sub-Region Grouping (MSRG), which clusters spatially adjacent patches with similar appearance into compact sub-region embeddings, reducing redundancy and forming semantically coherent morphological units. Sub-region graph is then constructed and processed by a lightweight Graph Neural Network (GNN) to model contextual dependencies and derive slide-level representations. Importantly, MSRG is a plug-and-play module that can be integrated into MIL, graph-based, and prototype-based pipelines, consistently improving their performance. Experiments on ten public benchmarks demonstrate that MorphoNet achieves superior performance on tumor subtyping and survival prediction. Our code is available at https://github.com/fuying-wang/MorphoNet .
Wang et al. (Sun,) studied this question.