MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models

2025-08-07 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical AI · Depth: Expert, extended

Summary

MedSIGHT is a unified framework designed to enhance medical large vision-language models (Med-LVLMs) with structured, pixel-level understanding for grounded visual comprehension and segmentation. It introduces a novel Region Perceiver module that generates region-centric tokens, encoding spatial information directly into the language model's representation space. A medical region codebook is integrated into the LLM vocabulary, enabling the generation of discrete region codes that symbolize anatomical and pathological regions, which are then decoded by the Region Perceiver to reconstruct segmentation masks. MedSIGHT employs a progressive training strategy to stably align these modules. Trained on only 72K multimodal instruction pairs, MedSIGHT achieves leading performance, with an average score of 62.3 on medical comprehension tasks, outperforming HuatuoGPT-Vision (58.3), and a mean Dice score of 69.9 on the new DiagSeg benchmark for grounded diagnostic segmentation across diverse imaging modalities. The model uses Qwen3-8B and UniMed-CLIP (ViT-L-14) as its backbones.

Key takeaway

For AI Scientists developing medical vision-language models, MedSIGHT demonstrates that unifying comprehension and segmentation is critical. You should consider integrating fine-grained spatial encoding via region-centric tokens and discrete, modality-aware codebooks into your LLM's vocabulary. This approach significantly improves diagnostic reasoning and pixel-level grounding, enabling more reliable and interpretable medical AI systems. Evaluate your models on joint diagnostic segmentation tasks like DiagSeg to ensure clinical relevance.

Key insights

MedSIGHT unifies medical visual comprehension and pixel-level segmentation via region-centric tokens and a modality-aware codebook.

Principles

• Fine-grained visual input improves medical LVLM grounding.
• Discrete region codes enhance LLM's expressive capacity.
• Progressive training aligns complex multimodal components.

Method

MedSIGHT uses a Region Perceiver for spatial encoding, a modality-aware codebook for discrete region representation, and a progressive multi-stage training pipeline for stable integration and unified instruction tuning.

In practice

• Integrate region-centric tokens for precise localization.
• Expand LLM vocabulary with discrete visual codes.
• Use multi-stage alignment for complex model integration.

Topics

• Medical LVLMs
• Visual Grounding
• Image Segmentation
• Region Perceiver
• Modality-aware Codebook
• Diagnostic AI

Code references

• Aofei-Chang/MedSIGHT
• ShawnHuang497/MedPLIB

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.