Advancing High Resolution Vision-Language Models in Biomedicine

Zekai Chen; Arda Pekis; Kevin Brown

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arXiv · 2406.09454

Advancing High Resolution Vision-Language Models in Biomedicine

Zekai Chen·Arda Pekis·Kevin Brown

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Abstract

Multi-modal learning has significantly advanced generative AI, especially in vision-language modeling. Innovations like GPT-4V and open-source projects such as LLaVA have enabled robust conversational agents capable of zero-shot task completions. However, applying these technologies in the biomedical field presents unique challenges. Recent initiatives like LLaVA-Med have started to adapt instruction-tuning for biomedical contexts using large datasets such as PMC-15M. Our research offers three key contributions: (i) we present a new instruct dataset enriched with medical image-text pairs from Claude3-Opus and LLaMA3 70B, (ii) we propose a novel image encoding strategy using hierarchical representations to improve fine-grained biomedical visual comprehension, and (iii) we develop the Llama3-Med model, which achieves state-of-the-art zero-shot performance on biomedical visual question answering benchmarks, with an average performance improvement of over 10% compared to previous methods. These advancements provide more accurate and reliable tools for medical professionals, bridging gaps in current multi-modal conversational assistants and promoting further innovations in medical AI.

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Zekai Chen, Arda Pekis, Kevin Brown. 2024-06-12. Advancing High Resolution Vision-Language Models in Biomedicine. https://arxiv.org/abs/2406.09454

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