SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Images

• URL: http://arxiv.org/abs/2509.00442v2
• Date: Sat, 27 Sep 2025 12:50:57 GMT
• Title: SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Images
• Authors: Lubin Gan, Xiaoman Wu, Jing Zhang, Zhifeng Wang, Linhao Qu, Siying Wu, Xiaoyan Sun,
• Abstract summary: SemaMIL is an adaptive method for extracting discriminative features from whole slide images.<n>It clusters semantically similar patches in sequence through a reversible permutation.<n>It achieves state-of-the-art subtype accuracy with fewer FLOPs and parameters.
• Score: 17.674866281320046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple instance learning (MIL) has become the leading approach for extracting discriminative features from whole slide images (WSIs) in computational pathology. Attention-based MIL methods can identify key patches but tend to overlook contextual relationships. Transformer models are able to model interactions but require quadratic computational cost and are prone to overfitting. State space models (SSMs) offer linear complexity, yet shuffling patch order disrupts histological meaning and reduces interpretability. In this work, we introduce SemaMIL, which integrates Semantic Reordering (SR), an adaptive method that clusters and arranges semantically similar patches in sequence through a reversible permutation, with a Semantic-guided Retrieval State Space Module (SRSM) that chooses a representative subset of queries to adjust state space parameters for improved global modeling. Evaluation on four WSI subtype datasets shows that, compared to strong baselines, SemaMIL achieves state-of-the-art accuracy with fewer FLOPs and parameters.

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