LLaVA-RadZ: Can Multimodal Large Language Models Effectively Tackle Zero-shot Radiology Recognition?

• URL: http://arxiv.org/abs/2503.07487v1
• Date: Mon, 10 Mar 2025 16:05:40 GMT
• Title: LLaVA-RadZ: Can Multimodal Large Language Models Effectively Tackle Zero-shot Radiology Recognition?
• Authors: Bangyan Li, Wenxuan Huang, Yunhang Shen, Yeqiang Wang, Shaohui Lin, Jingzhong Lin, Ling You, Yinqi Zhang, Ke Li, Xing Sun, Yuling Sun,
• Abstract summary: multimodal large models (MLLMs) have demonstrated exceptional capabilities in visual understanding and reasoning.<n>We propose LLaVA-RadZ, a framework for zero-shot medical disease recognition.<n>We introduce a Domain Knowledge Anchoring Module (DKAM) to exploit the intrinsic medical knowledge of large models.
• Score: 30.843971208278006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, multimodal large models (MLLMs) have demonstrated exceptional capabilities in visual understanding and reasoning across various vision-language tasks. However, MLLMs usually perform poorly in zero-shot medical disease recognition, as they do not fully exploit the captured features and available medical knowledge. To address this challenge, we propose LLaVA-RadZ, a simple yet effective framework for zero-shot medical disease recognition. Specifically, we design an end-to-end training strategy, termed Decoding-Side Feature Alignment Training (DFAT) to take advantage of the characteristics of the MLLM decoder architecture and incorporate modality-specific tokens tailored for different modalities, which effectively utilizes image and text representations and facilitates robust cross-modal alignment. Additionally, we introduce a Domain Knowledge Anchoring Module (DKAM) to exploit the intrinsic medical knowledge of large models, which mitigates the category semantic gap in image-text alignment. DKAM improves category-level alignment, allowing for accurate disease recognition. Extensive experiments on multiple benchmarks demonstrate that our LLaVA-RadZ significantly outperforms traditional MLLMs in zero-shot disease recognition and exhibits the state-of-the-art performance compared to the well-established and highly-optimized CLIP-based approaches.

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