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

## TL;DR

SemaMIL introduces a novel semantic-aware multiple instance learning framework for whole slide images, combining semantic reordering and retrieval-guided state space modeling to improve accuracy and efficiency in computational pathology.

## Contribution

It proposes a new method integrating semantic reordering and retrieval-guided state space modeling to enhance interpretability and performance in WSI analysis.

## Key findings

- Achieves state-of-the-art accuracy on four WSI datasets.
- Uses fewer FLOPs and parameters than baseline methods.
- Improves global modeling of histological features.

## 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.