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Recent learning-based approaches have made astonishing advances in calibratedmedical imaging like computerized tomography, yet they struggle to generalizein uncalibrated modalities -- notoriously magnetic resonance imaging (MRI),where performance is highly sensitive to the differences in MR contrast,resolution, and orientation between the training and testing data. Thisprevents broad applicability to the diverse clinical acquisition protocols inthe real world. We introduce Brain-ID, a robust feature representation learningstrategy for brain imaging, which is contrast-agnostic, and robust to the brainanatomy of each subject regardless of the appearance of acquired images (i.e.,deformation, contrast, resolution, orientation, artifacts, etc). Brain-ID istrained entirely on synthetic data, and easily adapts to downstream tasks withour proposed simple one-layer solution. We validate the robustness of Brain-IDfeatures, and evaluate their performance in a variety of downstreamapplications, including both contrast-independent (anatomyreconstruction/contrast synthesis, brain segmentation), and contrast-dependent(super-resolution, bias field estimation) tasks. Extensive experiments on 6public datasets demonstrate that Brain-ID achieves state-of-the-art performancein all tasks, and more importantly, preserves its performance when only limitedtraining data is available.

Brain-ID: Learning Robust Feature Representations for Brain Imaging