Gastric-X：用于推进胃癌分析中视觉语言模型的多模态多期基准数据集
Gastric-X: A Multimodal Multi-Phase Benchmark Dataset for Advancing Vision-Language Models in Gastric Cancer Analysis
为医学视觉语言模型提供了一个包含配对CT、内镜图像、生化指标和专家报告的多模态基准，可用于评测和开发面向真实临床工作流的VQA、报告生成、跨模态检索和病灶定位方法。
原文依据 we introduce Gastric-X, a large-scale multimodal benchmark for gastric cancer analysis providing 1.7K cases
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期视觉语言模型（VLM）在自然领域展现出强大的泛化能力和多模态推理能力。然而，由于缺乏能够反映真实临床工作流的全面且结构化的数据集，其在医学诊断中的应用仍然受限。为推进VLM在临床应用尤其是胃癌中的发展，我们提出了Gastric-X，一个用于胃癌分析的大规模多模态基准，包含1.7K个病例。Gastric-X中的每个病例均包含配对的静息期和动态期CT扫描、内镜图像、一组结构化生化指标、专家撰写的诊断报告以及肿瘤区域的边界框标注，反映了真实的临床条件。我们系统性地评估了近期VLM在五个核心任务上的能力：视觉问答（VQA）、报告生成、跨模态检索、疾病分类和病灶定位。这些任务模拟了临床工作流的关键阶段，从视觉理解与推理到多模态决策支持。通过这一评估，我们不仅旨在衡量模型性能，还试图探究VLM理解能力的本质：当前VLM能否有意义地将生化信号与空间肿瘤特征及文本报告相关联？我们期望Gastric-X能够成为使机器智能与医生的认知及证据推理过程相对齐的一步，并为下一代医学VLM的发展提供资源启发。
英文原文摘要 1512 字符
Recent vision-language models (VLMs) have shown strong generalization and multimodal reasoning abilities in natural domains. However, their application to medical diagnosis remains limited by the lack of comprehensive and structured datasets that capture real clinical workflows. To advance the development of VLMs for clinical applications, particularly in gastric cancer, we introduce Gastric-X, a large-scale multimodal benchmark for gastric cancer analysis providing 1.7K cases. Each case in Gastric-X includes paired resting and dynamic CT scans, endoscopic image, a set of structured biochemical indicators, expert-authored diagnostic notes, and bounding box annotations of tumor regions, reflecting realistic clinical conditions. We systematically examine the capability of recent VLMs on five core tasks: Visual Question Answering (VQA), report generation, cross-modal retrieval, disease classification, and lesion localization. These tasks simulate critical stages of clinical workflow, from visual understanding and reasoning to multimodal decision support. Through this evaluation, we aim not only to assess model performance but also to probe the nature of VLM understanding: Can current VLMs meaningfully correlate biochemical signals with spatial tumor features and textual reports? We envision Gastric-X as a step toward aligning machine intelligence with the cognitive and evidential reasoning processes of physicians, and as a resource to inspire the development of next-generation medical VLMs.