MedSG-Bench：一个面向医学图像序列定位的基准
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding
AI总结：
本文提出首个医学图像序列定位基准 MedSG-Bench，涵盖八类任务、76 个数据集与 9,630 个问答对，并配套 MedSG-188K 指令微调数据集及 MedSeq-Grounder 模型。
摘要：
视觉定位对于多模态大语言模型（MLLMs）的精确感知与推理至关重要，尤其是在医学影像领域。尽管现有医学视觉定位基准主要关注单图像场景，但真实临床应用往往涉及序列图像：在不同模态之间准确进行病灶定位，以及对疾病进展进行时间追踪（例如治疗前与治疗后的比较），都需要细粒度的跨图像语义对齐和具备上下文感知能力的推理。为弥补现有医学视觉定位基准中图像序列代表性不足的问题，我们提出 MedSG-Bench，这是首个专为医学图像序列定位定制的基准。它包含八个 VQA 风格任务，并被组织为两种定位任务范式：1）图像差异定位，聚焦检测图像之间的变化区域；2）图像一致性定位，强调检测序列图像中一致或共享的语义。MedSG-Bench 覆盖 76 个公开数据集、10 种医学影像模态，以及广泛的解剖结构和疾病，共计 9,630 个问答对。我们对通用 MLLMs（例如 Qwen2.5-VL）和医学领域专用 MLLMs（例如 HuatuoGPT-vision）进行了基准评测，观察到即使是先进模型在医学序列定位任务中也表现出明显局限。为推动该领域发展，我们构建了 MedSG-188K，这是一个专为序列视觉定位定制的大规模指令微调数据集，并进一步开发了 MedSeq-Grounder，这是一个旨在促进未来医学序列图像细粒度理解研究的 MLLM。该基准、数据集和模型可在 https://huggingface.co/MedSG-Bench 获取。
英文摘要：
Visual grounding is essential for precise perception and reasoning in multimodal large language models (MLLMs), especially in medical imaging domains. While existing medical visual grounding benchmarks primarily focus on single-image scenarios, real-world clinical applications often involve sequential images, where accurate lesion localization across different modalities and temporal tracking of disease progression (e.g., pre- vs. post-treatment comparison) require fine-grained cross-image semantic alignment and context-aware reasoning. To remedy the underrepresentation of image sequences in existing medical visual grounding benchmarks, we propose MedSG-Bench, the first benchmark tailored for Medical Image Sequences Grounding. It comprises eight VQA-style tasks, formulated into two paradigms of the grounding tasks, including 1) Image Difference Grounding, which focuses on detecting change regions across images, and 2) Image Consistency Grounding, which emphasizes detection of consistent or shared semantics across sequential images. MedSG-Bench covers 76 public datasets, 10 medical imaging modalities, and a wide spectrum of anatomical structures and diseases, totaling 9,630 question-answer pairs. We benchmark both general-purpose MLLMs (e.g., Qwen2.5-VL) and medical-domain specialized MLLMs (e.g., HuatuoGPT-vision), observing that even the advanced models exhibit substantial limitations in medical sequential grounding tasks. To advance this field, we construct MedSG-188K, a large-scale instruction-tuning dataset tailored for sequential visual grounding, and further develop MedSeq-Grounder, an MLLM designed to facilitate future research on fine-grained understanding across medical sequential images. The benchmark, dataset, and model are available at https://huggingface.co/MedSG-Bench
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
