The Impact of Image Resolution on Biomedical Multimodal Large Language Models
This work addresses the problem of information loss in biomedical image analysis for researchers and clinicians, offering incremental improvements to optimize MLLMs for high-resolution medical imaging.
The study investigated how image resolution affects biomedical multimodal large language models (MLLMs), finding that native-resolution training and inference significantly improve performance across tasks, while misalignment between resolutions degrades it, and mixed-resolution training effectively balances computational constraints with performance.
Imaging technologies are fundamental to biomedical research and modern medicine, requiring analysis of high-resolution images across various modalities. While multimodal large language models (MLLMs) show promise for biomedical image analysis, most are designed for low-resolution images from general-purpose datasets, risking critical information loss. We investigate how image resolution affects MLLM performance in biomedical applications and demonstrate that: (1) native-resolution training and inference significantly improve performance across multiple tasks, (2) misalignment between training and inference resolutions severely degrades performance, and (3) mixed-resolution training effectively mitigates misalignment and balances computational constraints with performance requirements. Based on these findings, we recommend prioritizing native-resolution inference and mixed-resolution datasets to optimize biomedical MLLMs for transformative impact in scientific research and clinical applications.