[论文解读] MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging

Leonard Nürnberg, Dennis Bontempi+18 more|arXiv (Cornell University)|Jan 15, 2026

Radiomics and Machine Learning in Medical Imaging被引用 0

一句话总结

MHub.ai 引入一个开源、基于容器的平台，标准化对医疗影像AI模型的访问，增加参考数据、统一接口和仪表板，以促进重复性和基准测试。

ABSTRACT

Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are limited by the wide variety of AI implementations and architectures, inconsistent documentation, and reproducibility issues. Here, we introduce MHub$.$ai, an open-source, container-based platform that standardizes access to AI models with minimal configuration, promoting accessibility and reproducibility in medical imaging. MHub$.$ai packages models from peer-reviewed publications into standardized containers that support direct processing of DICOM and other formats, provide a unified application interface, and embed structured metadata. Each model is accompanied by publicly available reference data that can be used to confirm model operation. MHub$.$ai includes an initial set of state-of-the-art segmentation, prediction, and feature extraction models for different modalities. The modular framework enables adaptation of any model and supports community contributions. We demonstrate the utility of the platform in a clinical use case through comparative evaluation of lung segmentation models. To further strengthen transparency and reproducibility, we publicly release the generated segmentations and evaluation metrics and provide interactive dashboards that allow readers to inspect individual cases and reproduce or extend our analysis. By simplifying model use, MHub$.$ai enables side-by-side benchmarking with identical execution commands and standardized outputs, and lowers the barrier to clinical translation.

研究动机与目标

• 推动在医疗影像中对标准化、可重复的 AI 模型访问的需求。
• 描述一个将模型打包到标准化容器中的开源平台。
• 演示该平台如何支持对 DICOM 等格式的直接处理。
• 解释嵌入的元数据和参考数据如何实现透明评估与再利用。
• 展示一个临床用例，以说明基准测试和可重复性收益。

提出的方法

• 将来自同行评议论文的模型打包为标准化容器。
• 提供统一的应用接口并支持对 DICOM 及其他格式的直接处理。
• 嵌入结构化元数据并为每个模型提供公开可用的参考数据。
• 促成社区贡献并实现对任意模型的模块化适配。

实验结果

研究问题

• RQ1 MHub.ai 如何在最小配置下标准化访问医疗影像中的 AI 模型？
• RQ2 标准化容器和参考数据是否能够实现跨模型的透明基准测试和可重复性？
• RQ3 统一接口和仪表板对临床环境中模型评估和再利用有何影响？
• RQ4 社区贡献在多大程度上能够集成到平台中以扩展模型覆盖范围？

主要发现

• 来自同行评议论文的模型被打包为标准化容器。
• 该平台支持对 DICOM 及其他格式的直接处理，具备统一接口。
• 每个模型都包含嵌入式元数据和公开可用的参考数据以用于验证。
• 生成的分割和评估指标被公开发布以促进透明度。
• 交互式仪表板允许对个案进行检查并重现或扩展分析。
• 该框架实现了在相同执行命令和输出下的并排基准对比。