Making deep learning models in medicine available through a standardized I/O framework.
MHub is a repository of self-contained deep-learning models trained for a wide variety of applications in the medical and medical imaging domain. With a strong emphasis on cutting-edge advancements and reproducible science, we empower AI researchers to access fully reproducible and portable model implementations, enabling them to drive innovation in healthcare.
At MHub, we are driven by a vision: to revolutionize the medical field through the application of artificial intelligence. We recognize the immense potential that deep learning holds in transforming healthcare, and our mission is to make these advanced technologies readily available to researchers, practitioners, and industry professionals.
In our repository, you will find a collection of deep-learning models sourced from prominent literature in the field. The models we include have been meticulously curated, optimized, and packaged in MHub containers. MHub is framework agnostic and supports all numerical computing backends, providing the community with AI pipelines that work out of the box as intended by the developers.
Our team stays up-to-date with the latest research and best practices in the field, ensuring that the models we offer are always aligned with the most recent advancements. A vast majority of the pipelines we host are accompanied by peer-reviewed studies published in scientific journals. Integrating MHub models into your analysis pipeline translates to incorporating AI models whose results are backed by rigorous research and academic scrutiny.
We prioritize the principles of reproducible science and knowledge sharing. We understand the significance of transparency and collaboration in advancing the field of AI in healthcare. To facilitate this, we provide meticulous documentation, examples, and tutorials alongside the models sourced from published literature. By offering these resources, we empower researchers to validate and build upon existing work, fostering a culture of scientific reproducibility and advancing medical AI.