为医学影像AI打造可机器验证的数据集标准，解决标注溯源与质量缺失问题。
VIDS: A Verified Imaging Dataset Standard for Medical AI
- 定义文件结构、命名规范与21条机器可验证规则，覆盖标注溯源与质量文档
- 四大公开数据集仅满足20%–39%标准，标注来源与质量记录是最大短板
- 提供100例经四位放射科医生共识标注的参考数据集，支持主流模型训练
医学影像AI的发展依赖于标注数据集，但现有标准未在统一框架内实现数据结构、标注溯源、质量文档和机器学习就绪性的可机器验证。DICOM标准化了单个研究层面的图像获取、存储与通信，BIDS则统一神经影像研究数据集的命名规范，二者均未涵盖数据整理层——即谁在何时用何种工具进行标注，以及达到何种质量标准。本文提出VIDS（Verified Imaging Dataset Standard），一个开放规范，定义了文件夹布局、文件命名、标注溯源模式、质量文档及两类合规性配置下的21条机器可验证规则。VIDS采用NIfTI作为标准工作格式，同时通过侧边文件保留完整的DICOM元数据以确保可追溯性，并支持导出至任何下游ML框架（如nnU-Net、MONAI、COCO、扁平NIfTI）而不丢失溯源信息。共定义22个合规维度，对四个主要公共数据集（LIDC-IDRI、BraTS、CheXpert、Medical Segmentation Decathlon）进行基准测试，结果显示即使广泛使用的数据集也仅满足20%–39%的维度，其中标注溯源与质量文档为系统性短板。发布了一个包含100例受试者的LIDC-Hybrid-100 VIDS合规参考CT数据集，由四位放射科医生共同标注的分割掩码（平均两两Dice系数为0.7765），在全合规配置下通过21/21项验证。VIDS完全开源：规范采用CC BY 4.0许可，所有工具采用Apache 2.0许可，参考验证器可在PyPI安装（pip install vids-validator），LIDC-Hybrid-100已发布于Zenodo（https://doi.org/10.5281/zenodo.19582717）。
原文摘要 · Abstract (English)
Medical imaging AI development is fundamentally dependent on annotated datasets, yet no existing standard provides machine-enforceable validation across dataset structure, annotation provenance, quality documentation, and ML readiness within a single framework. DICOM standardizes image acquisition, storage, and communication at the individual study level. BIDS organizes neuroimaging research datasets with consistent naming conventions. Neither addresses the curation layer, viz., who annotated what, when, with what tool, and to what quality standard. This paper presents VIDS (Verified Imaging Dataset Standard), an open specification that defines folder layout, file naming, annotation provenance schemas, quality documentation, and 21 machine-enforceable validation rules across two compliance profiles. VIDS uses NIfTI as a canonical working format while preserving full DICOM metadata in sidecars for traceability, and supports export to any downstream ML framework (nnU-Net, MONAI, COCO, flat NIfTI) without loss of provenance. Twenty-two compliance dimensions are defined and four major public datasets -- LIDC-IDRI, BraTS, CheXpert, and the Medical Segmentation Decathlon -- are benchmarked against these dimensions. Even widely used datasets satisfy only 20--39% of these dimensions, with provenance and quality documentation as the largest systematic gaps. LIDC-Hybrid-100 is released as a 100-subject VIDS-compliant reference CT dataset with consensus segmentation masks from four radiologist annotations (mean pairwise Dice 0.7765), validating 21/21 on the Full compliance profile. VIDS is fully open source: the specification is CC BY 4.0, all tools are Apache 2.0, the reference validator is available on PyPI (pip install vids-validator), and LIDC-Hybrid-100 is published on Zenodo (https://doi.org/10.5281/zenodo.19582717).
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