MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification

• URL: http://arxiv.org/abs/2602.14509v1
• Date: Mon, 16 Feb 2026 06:43:36 GMT
• Title: MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification
• Authors: Mingrui Ma, Chentao Li, Pan Huang, Jing Qin,
• Abstract summary: Clustering-based approaches can provide explainable decision-making process but suffer from high dimension features and semantically ambiguous centroids.<n>We propose an end-to-end MIL framework that integrates Grassmann re-embedding and manifold adaptive clustering.<n> Experiments on multicentre WSI datasets demonstrate that: 1) our cluster-incorporated model achieves superior performance in both grading accuracy and interpretability; 2) end-to-end learning refines better feature representations and it requires acceptable resources.
• Score: 9.952997875404634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whole slide images (WSIs) are the gold standard for pathological diagnosis and sub-typing. Current main-stream two-step frameworks employ offline feature encoders trained without domain-specific knowledge. Among them, attention-based multiple instance learning (MIL) methods are outcome-oriented and offer limited interpretability. Clustering-based approaches can provide explainable decision-making process but suffer from high dimension features and semantically ambiguous centroids. To this end, we propose an end-to-end MIL framework that integrates Grassmann re-embedding and manifold adaptive clustering, where the manifold geometric structure facilitates robust clustering results. Furthermore, we design a prior knowledge guiding proxy instance labeling and aggregation strategy to approximate patch labels and focus on pathologically relevant tumor regions. Experiments on multicentre WSI datasets demonstrate that: 1) our cluster-incorporated model achieves superior performance in both grading accuracy and interpretability; 2) end-to-end learning refines better feature representations and it requires acceptable computation resources.

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