MR Acquisition-Invariant Representation Learning
This addresses the lack of generalization in medical imaging for researchers and clinicians, but it is incremental as it builds on existing representation learning methods.
The paper tackles the problem of poor generalization in brain MRI tissue classification due to scanner differences by proposing a Siamese neural network (MRAI-net) to learn an acquisition-invariant representation, resulting in a linear classifier outperforming supervised CNNs when limited target data is available.
Voxelwise classification approaches are popular and effective methods for tissue quantification in brain magnetic resonance imaging (MRI) scans. However, generalization of these approaches is hampered by large differences between sets of MRI scans such as differences in field strength, vendor or acquisition protocols. Due to this acquisition related variation, classifiers trained on data from a specific scanner fail or under-perform when applied to data that was acquired differently. In order to address this lack of generalization, we propose a Siamese neural network (MRAI-net) to learn a representation that minimizes the between-scanner variation, while maintaining the contrast between brain tissues necessary for brain tissue quantification. The proposed MRAI-net was evaluated on both simulated and real MRI data. After learning the MR acquisition invariant representation, any supervised classification model that uses feature vectors can be applied. In this paper, we provide a proof of principle, which shows that a linear classifier applied on the MRAI representation is able to outperform supervised convolutional neural network classifiers for tissue classification when little target training data is available.