Gastric-X：一种多模态多阶段基准数据集，用于推进胃癌分析中的视觉-语言模型
Gastric-X: A Multimodal Multi-Phase Benchmark Dataset for Advancing Vision-Language Models in Gastric Cancer Analysis
AI总结：
Gastric-X通过1700例胃癌病例数据，评估视觉-语言模型在胃癌诊断中的多阶段任务能力，推动医疗领域VLM发展。
摘要：
近期视觉-语言模型（VLMs）在自然领域展现出强大的泛化和多模态推理能力，但在医疗诊断应用中受限于缺乏涵盖真实临床流程的综合性数据集。为推进VLM在临床应用中的发展，特别是胃癌领域，我们引入Gastric-X，一个大规模多模态基准数据集，包含1700例病例。每个病例包含配对的静息和动态CT扫描、内镜图像、一组结构化的生化指标、专家撰写的诊断笔记以及肿瘤区域的边界框标注，反映了真实的临床条件。我们系统地检验了最近VLMs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、疾病分类和病变定位。这些任务模拟了临床工作流程的关键阶段，从视觉理解和推理到多模态决策支持。通过此次评估，我们不仅评估模型性能，还探究VLM理解的本质：当前VLMs是否能有意义地关联生化信号与空间肿瘤特征和文本报告？我们设想Gastric-X是迈向将机器智能与医生的认知和证据推理过程对齐的一步，并作为激励下一代医疗VLMs发展的资源。
英文摘要：
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.
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