HGP-Mamba: Integrating Histology and Generated Protein Features for Mamba-based Multimodal Survival Risk Prediction
Jing Dai, Chen Wu, Ming Wu, Qibin Zhang, Zexi Wu, Jingdong Zhang, Hongming Xu

TL;DR
HGP-Mamba is a multimodal framework that combines histology images and generated protein features using advanced Mamba-based models to improve cancer survival risk prediction efficiently and accurately.
Contribution
It introduces a novel protein feature extractor from WSIs and two interaction modules, LiAM and GiEM, for enhanced cross-modal feature fusion in survival analysis.
Findings
Achieves state-of-the-art performance on four cancer datasets.
Demonstrates superior computational efficiency over existing methods.
Effectively integrates molecular and morphological data for prognosis.
Abstract
Recent advances in multimodal learning have significantly improved cancer survival risk prediction. However, the joint prognostic potential of protein markers and histopathology images remains underexplored, largely due to the high cost and limited availability of protein expression profiling. To address this challenge, we propose HGP-Mamba, a Mamba-based multimodal framework that efficiently integrates histological with generated protein features for survival risk prediction. Specifically, we introduce a protein feature extractor (PFE) that leverages pretrained foundation models to derive high-throughput protein embeddings directly from Whole Slide Images (WSIs), enabling data-efficient incorporation of molecular information. Together with histology embeddings that capture morphological patterns, we further introduce the Local Interaction-aware Mamba (LiAM) for fine-grained feature…
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Taxonomy
TopicsAI in cancer detection · Cell Image Analysis Techniques · Radiomics and Machine Learning in Medical Imaging
