Hilbert-VLM for Enhanced Medical Diagnosis
Published:Dec 30, 2025 06:18
•1 min read
•ArXiv
Analysis
This paper addresses the challenges of using Visual Language Models (VLMs) for medical diagnosis, specifically the processing of complex 3D multimodal medical images. The authors propose a novel two-stage fusion framework, Hilbert-VLM, which integrates a modified Segment Anything Model 2 (SAM2) with a VLM. The key innovation is the use of Hilbert space-filling curves within the Mamba State Space Model (SSM) to preserve spatial locality in 3D data, along with a novel cross-attention mechanism and a scale-aware decoder. This approach aims to improve the accuracy and reliability of VLM-based medical analysis by better integrating complementary information and capturing fine-grained details.
Key Takeaways
- •Proposes Hilbert-VLM, a novel framework for medical diagnosis using VLMs.
- •Integrates Hilbert space-filling curves into the Mamba SSM for improved spatial locality.
- •Introduces a novel Hilbert-Mamba Cross-Attention mechanism and a scale-aware decoder.
- •Achieves promising results on the BraTS2021 benchmark, demonstrating potential for improved accuracy and reliability in medical VLM-based analysis.
Reference
“The Hilbert-VLM model achieves a Dice score of 82.35 percent on the BraTS2021 segmentation benchmark, with a diagnostic classification accuracy (ACC) of 78.85 percent.”