Recent learning-based approaches have made astonishing advances in calibrated
medical imaging like computerized tomography, yet they struggle to generalize
in 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. This
prevents broad applicability to the diverse clinical acquisition protocols in
the real world. We introduce Brain-ID, a robust feature representation learning
strategy for brain imaging, which is contrast-agnostic, and robust to the brain
anatomy of each subject regardless of the appearance of acquired images (i.e.,
deformation, contrast, resolution, orientation, artifacts, etc). Brain-ID is
trained entirely on synthetic data, and easily adapts to downstream tasks with
our proposed simple one-layer solution. We validate the robustness of Brain-ID
features, and evaluate their performance in a variety of downstream
applications, including both contrast-independent (anatomy
reconstruction/contrast synthesis, brain segmentation), and contrast-dependent
(super-resolution, bias field estimation) tasks. Extensive experiments on 6
public datasets demonstrate that Brain-ID achieves state-of-the-art performance
in all tasks, and more importantly, preserves its performance when only limited
training data is available.

Brain-ID 是一种用于脑成像的稳健特征表示学习策略，无论采集图像的外观如何（例如变形、对比度、分辨率、方向、伪影等），都能适应各个受试者的脑解剖结构，可用于多种对比度相关和独立的下游任务。