  Magnetic Resonance Imaging (MRI) is considered the gold standard of medical
imaging because of the excellent soft-tissue contrast exhibited in the images
reconstructed by the MRI pipeline, which in-turn enables the human radiologist
to discern many pathologies easily. More recently, Deep Learning (DL) models
have also achieved state-of-the-art performance in diagnosing multiple diseases
using these reconstructed images as input. However, the image reconstruction
process within the MRI pipeline, which requires the use of complex hardware and
adjustment of a large number of scanner parameters, is highly susceptible to
noise of various forms, resulting in arbitrary artifacts within the images.
Furthermore, the noise distribution is not stationary and varies within a
machine, across machines, and patients, leading to varying artifacts within the
images. Unfortunately, DL models are quite sensitive to these varying artifacts
as it leads to changes in the input data distribution between the training and
testing phases. The lack of robustness of these models against varying
artifacts impedes their use in medical applications where safety is critical.
In this work, we focus on improving the generalization performance of these
models in the presence of multiple varying artifacts that manifest due to the
complexity of the MR data acquisition. In our experiments, we observe that
Batch Normalization, a widely used technique during the training of DL models
for medical image analysis, is a significant cause of performance degradation
in these changing environments. As a solution, we propose to use other
normalization techniques, such as Group Normalization and Layer Normalization
(LN), to inject robustness into model performance against varying image
artifacts. Through a systematic set of experiments, we show that GN and LN
provide better accuracy for various MR artifacts and distribution shifts.
