构建多模态膝关节半月板损伤评估基准，融合影像与临床信息提升诊断准确性。
MeniOmni: A Structured Multimodal Benchmark for Holistic Meniscus Injury Assessment
- 融合MRI影像与患者性别、年龄等临床先验信息进行综合诊断。
- 在746例多中心数据上实现精细分级与结构化报告生成。
- 提出临床相关性更强的评估指标，助力安全精准诊断。
半月板损伤的临床诊断需结合三维MRI影像、患者临床背景（如性别、年龄、体重指数）并生成结构化报告。现有膝关节MRI基准多为单模态且依赖粗略标签，难以评估整体临床推理能力。我们提出MeniOmni，一个结构化的多模态半月板损伤评估基准，包含746例多中心MRI数据，提供三平面体积输入、临床先验信息及专家标注的临床文本。该基准支持两项任务：(1) 精细的Stoller严重程度分级；(2) 诊断报告生成。我们进一步提出风险感知的序数评估方法和语义一致性度量（Meni-Score），更贴近临床实际。基线实验表明，引入临床先验可提升分级性能并减少严重误判，凸显多模态上下文对安全评估的价值。代码与数据见https://github.com/ShuruiXu/MeniOmni。
原文摘要 · Abstract (English)
Clinical diagnosis of meniscus injuries requires radiologists to integrate volumetric MRI evidence with patient context (e.g., sex, age, BMI) and to produce structured diagnostic reports. Existing knee MRI benchmarks are typically unimodal and rely on coarse labels, limiting their ability to evaluate holistic clinical reasoning. We introduce MeniOmni, a structured multimodal benchmark for meniscus injury assessment, consisting of 746 multi-center MRI studies with tri-planar volumetric inputs, Clinical Priors, and expert-annotated clinical text. MeniOmni supports two tasks: (1) fine-grained Stoller severity grading and (2) diagnostic report generation. We further propose risk-aware ordinal evaluation and a semantic consistency metric (Meni-Score) to better reflect clinical relevance. Baseline experiments show that incorporating Clinical Priors improves grading performance and reduces severe errors, highlighting the value of multimodal context for safer assessment. Code and data are available at https://github.com/ShuruiXu/MeniOmni.
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