Advancing High Resolution Vision-Language Models in Biomedicine

• URL: http://arxiv.org/abs/2406.09454v1
• Date: Wed, 12 Jun 2024 18:29:26 GMT
• Title: Advancing High Resolution Vision-Language Models in Biomedicine
• Authors: Zekai Chen, Arda Pekis, Kevin Brown,
• Abstract summary: We present a new instruct dataset enriched with medical image-text pairs from Claude3-Opus and LLaMA3 70B. We develop the Llama3-Med model, which achieves state-of-the-art zero-shot performance on biomedical visual question answering benchmarks.
• Score: 4.514292200785639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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