MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging
Leonard N\"urnberg, Dennis Bontempi, Suraj Pai, Curtis Lisle, Steve Pieper, Ron Kikinis, Sil van de Leemput, Rahul Soni, Gowtham Murugesan, Cosmin Ciausu, Miriam Groeneveld, Felix J. Dorfner, Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan, Joeran S. Bosma, Keno Bressem

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 MHubai, an open-source, container-based platform that standardizes access to AI models with minimal configuration, promoting accessibility and reproducibility in medical imaging. MHubai 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. MHubai includes an initial set of state-of-the-art segmentation,…
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Taxonomy
TopicsRadiomics and Machine Learning in Medical Imaging · Artificial Intelligence in Healthcare and Education · AI in cancer detection
