aiqbench analysis
The introduction of the MMBU benchmark represents a significant advancement in evaluating Vision-Language Models specifically for complex biomedical imaging tasks across 35 submodalities. This research highlights critical gaps in current models regarding visual perception and domain generalization, moving beyond simple accuracy metrics to stress-test frontier AI systems with hard, held-out datasets. By establishing a comprehensive standard for medical image analysis, this work directly addresses the need for robust AI integration in clinical workflows.
MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models
arXiv:2606.06696v2 Announce Type: replace-cross Abstract: Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling cellular features in microscopy. Realizing this potential, however, requires robust and fine-grained visual perception. Models need to correctly interpret subtle features in images, and they must do so across diverse biomedical modalities, scales, and contexts. Nevertheless, current benchmarks remain limited. To address these gaps, we introduce the Massive Multimodal Biomedical Understanding (MMBU) benchmark. It is the largest biomedical vision and language benchmark to date, covering 35 submodalities with rich structured metadata. It includes both open and closed versions of ungrounded classification, grounded classification, and object detection, enabling systematic evaluation of model performance across biological scales, clinical settings, and imaging modalities. Evaluating 16 open-weight and 5 frontier VLMs in the main comparison, we find that while medical adaptation provides measurable gains for some models, the high accuracy often reported on established benchmarks can mask deficiencies in visual perception and domain generalization. We further define an open-ended MMBU hard set, split into a released public subset and a held-out private subset, to stress-test frontier models.
Authors:Ryan D'Cunha, Alejandro Lozano, Xiaoxiao Sun, Daniel Vela Jarquin, Min Woo Sun, Josiah Aklilu, James Burgess, Yuhui Zhang, Mark Endo, Ryan Nayebi, Akira Nishii, Maximilian Rokuss, Paola Avila Robayo, Jin Ye, Ming Hu, Zhongying Deng, Junjun He, Xin Chen, Yue Yao, Robert Tibshirani, Jeffrey J. Nirschl, Serena Yeung-Levy
Abstract:Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling cellular features in microscopy. Realizing this potential, however, requires robust and fine-grained visual perception. Models need to correctly interpret subtle features in images, and they must do so across diverse biomedical modalities, scales, and contexts. Nevertheless, current benchmarks remain limited. To address these gaps, we introduce the Massive Multimodal Biomedical Understanding (MMBU) benchmark. It is the largest biomedical vision and language benchmark to date, covering 35 submodalities with rich structured metadata. It includes both open and closed versions of ungrounded classification, grounded classification, and object detection, enabling systematic evaluation of model performance across biological scales, clinical settings, and imaging modalities. Evaluating 16 open-weight and 5 frontier VLMs in the main comparison, we find that while medical adaptation provides measurable gains for some models, the high accuracy often reported on established benchmarks can mask deficiencies in visual perception and domain generalization. We further define an open-ended MMBU hard set, split into a released public subset and a held-out private subset, to stress-test frontier models.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2606.06696 [cs.CV]
(or arXiv:2606.06696v2 [cs.CV] for this version)
Full text under the publisher's open licence · 249 words
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