MedFusion-Mamba: Redefining Clinical AI with Segmentation-Guided Multimodal Deep Learning for Superior Disease Prediction

Abstract

Accurate and reliable disease prediction in clinical settings requires models that can adapt to heterogeneous data sources and maintain robustness across diverse environments. The proposed framework, MedFusion-Mamba, introduces a hybrid deep learning approach that integrates foundation model-based anatomical segmentation, self-supervised visual feature extraction, state-space sequence modeling, and tabular EHR fusion into a unified architecture. The design enables enhanced focus on relevant anatomical structures, improved generalization with minimal labeled data, and effective exploitation of temporal or volumetric imaging information. The integration of structured clinical data further strengthens predictive capabilities, while adaptive mechanisms at inference time ensure resilience against domain shifts. Evaluations target multi-label thoracic disease prediction and multimodal clinical outcome forecasting, emphasizing both performance accuracy and interpretability. The architecture aims to advance predictive healthcare by offering a robust, efficient, and transparent solution adaptable to diverse clinical contexts.

Introduction

Deep learning has significantly advanced medical diagnostics, especially in imaging domains like radiology and pathology. However, real-world deployment faces challenges due to variability in imaging protocols and limited labeled datasets. The MedFusion-Mamba framework is proposed to address these issues by integrating advanced techniques for robust, generalizable, and interpretable predictions.

Key Components of MedFusion-Mamba:

1. Foundation Model Segmentation:
   Uses Medical-SAM-2 to extract disease-relevant regions, reducing background noise.

2. Self-Supervised Feature Extraction:
   Employs DINOv2 Vision Transformers to learn rich image features from ROI patches without labels.

3. State-Space Sequence Modeling:
   Applies Mamba, a state-space model, to process volumetric/temporal imaging data efficiently.

4. EHR Feature Modeling:
   Leverages FT-Transformer to model structured clinical data (EHR).

5. Multimodal Fusion & Prediction:
   Combines image and EHR features using gated cross-attention for final predictions.

6. Reliability Enhancements:
   Uses test-time entropy minimization and conformal prediction for calibration and domain adaptation.

Datasets & Protocols:

• Training: CheXpert (imaging), MIMIC-IV (EHR)

• Testing: MIMIC-CXR (cross-domain evaluation)

• Preprocessing includes segmentation, normalization, encoding, and missing data imputation.

Experimental Results:

• Evaluation Metrics:
  Macro-AUROC, macro-AUPRC, F1, ECE (Expected Calibration Error), robustness drop, and conformal coverage-risk curves.

• Ablation Findings:

  • Segmentation: +2.1 AUROC

  • SSL (DINOv2): +3.4 AUROC

  • State-space modeling: +2.7 AUROC

  • EHR fusion: +1.9 AUROC

  • Reliability layers: ↓ 38% ECE

• Robustness:
  Maintains >90% performance under noise, cross-domain shifts, and missing data scenarios.

• Efficiency:

Conclusion

The MedFusion-Mamba framework introduced in this study demonstrates that combining segmentation-guided vision encoders, self-supervised learning backbones, state-space modeling, and multimodal fusion can substantially advance the predictive accuracy, reliability, and interpretability of clinical AI systems. By addressing core challenges such as limited annotated datasets, domain variability, and poor calibration, the approach achieved superior AUROC and AUPRC scores compared to established baselines, while maintaining a high degree of probability reliability. The modular nature of the architecture ensures adaptability to a broad range of medical conditions and diagnostic modalities, enabling its potential application in varied clinical domains. The observed performance improvements validate the benefits of integrating imaging and structured EHR data, demonstrating that comprehensive patient profiles yield more precise and trustworthy predictions. Future work will focus on three main directions. First, large-scale cross-institutional validation will be conducted to evaluate the model’s generalizability across diverse patient populations, imaging equipment, and clinical protocols. Second, optimizations will be introduced to reduce computational overhead, enabling real-time inference and deployment in resource-constrained healthcare environments. Third, integration with explainable AI techniques will be pursued to enhance transparency, providing clinicians with interpretable decision pathways that align with medical reasoning. In conclusion, MedFusion-Mamba represents a promising step toward AI systems that not only achieve state-of-the-art performance in disease prediction but also meet the operational, ethical, and trustworthiness requirements essential for adoption in modern clinical practice.

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Copyright

Copyright © 2025 Dr. Aditi Tulchhia, Ankit Porwal, Deepak Chhaparwal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.