Cooper, PhD, associate professor of pathology and director, Computational Pathology at the Northwestern University Feinberg School of Medicine. This is an important step in making AI a universal tool for pathology research,” said Lee A.D. “MONAI will enable pathologists and scientists to build accurate models without knowing anything about AI. New features, sample applications, and viewer integrations for annotating pathology images create a starting point for pathologists and data scientists to work together to use the benefits of deep learning. Last year, MONAI Label expanded into the field of pathology.

With the release of MONAI 1.0 in September 2022, you can now access 21 of the leading medical imaging models for tasks such as MRI segmentation, breast-density classification, and pathology tumor detection in MONAI Model Zoo. Over 700 GitHub projects are based on MONAI today. Swim-UNETR is a state-of-the-art, 3D transformer-based model with self-supervised pretraining. Swin-UNETR, another research breakthrough from the MONAI community, was published in CVPR with authors from Vanderbilt University and NVIDIA. Researchers in the MONAI community finished atop the leaderboard using Auto3DSeg in numerous segmentation competitions like the MICCAI INSTANCE22 and HECKTOR22 challenges. It enables data scientists and researchers of any skill to train models that can quickly segment regions of interest in 3D imaging modalities like CT and MRI. The most notable is Auto3DSeg, a low-code framework. The 1M mark represents a major milestone for the medical open network for AI, which has powered numerous research breakthroughs and introduced new developer tools in the last year.

MONAI, the domain-specific, open-source medical imaging AI framework that drives research breakthroughs and accelerates AI into clinical impact, has now been downloaded by over 1M data scientists, developers, researchers, and clinicians.

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