FastMRI open source tools from Facebook and NYU
Facebook AI Research (FAIR) and NYU School of Medicine’s Center for Advanced Imaging Innovation and Research (CAI²R) are sharing new open source tools and data as part of fastMRI, a joint research project to spur development of AI systems to speed MRI scans by up to 10x. Today’s releases include new AI models and baselines for this task(as described in our paper here). It also includes the first large-scale MRI data set of its kind, which can serve as a benchmark for future research.
By sharing a standardized set of AI tools and MRI data, as well as hosting a leaderboard where research teams can compare their results, we aim to help improve diagnostic imaging technology, and eventually increase patients’ access to a powerful and sometimes life-saving technology. With new AI techniques, we hope to generate scans that require much less measurement data to produce the image detail necessary for accurate detection of abnormalities. Sharing this suite of resources reflects the fastMRI mission, which is to engage the larger community of AI and medical imaging researchers rather than to develop proprietary methods for accelerating MR imaging.
Improved accuracy with increased training data: This chart shows the impact of four sizes of training sets on the performance of our baseline ML models, which used a U-net neural network architecture. The more MRI cases the system was trained on, the lower its loss was, indicating that its image predictions were more likely to be accurate.