Hilbert-VLM for Enhanced Medical Diagnosis
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 / Citation
View Original"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."