MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging

TL;DR

MHub.ai is an open-source platform that standardizes and simplifies access to AI models in medical imaging, enhancing reproducibility, benchmarking, and clinical translation through containerization and community collaboration.

Contribution

It introduces a container-based, standardized platform for AI models in medical imaging, supporting reproducibility, benchmarking, and community contributions.

Findings

Demonstrated utility in lung segmentation case study.

Enabled reproducible benchmarking with standardized outputs.

Facilitated community contributions and model adaptation.

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.