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.

翻译：磁共振成像（MRI）因其卓越的软组织对比度而成为医学影像的黄金标准，这种对比度使得放射科医生能够轻松识别多种病理特征。近年来，深度学习（DL）模型利用这些重建图像作为输入，在多种疾病诊断中也取得了最先进的性能。然而，MRI成像链中的图像重建过程需要使用复杂硬件并调整大量扫描参数，极易受到各种噪声的影响，导致图像中出现随机伪影。此外，噪声分布并非稳态，会在同一设备内、不同设备间及不同患者间产生变化，从而在图像中造成多样化的伪影。不幸的是，深度学习模型对这些变化伪影相当敏感，因为这会导致训练与测试阶段的输入数据分布发生偏移。这些模型对变化伪影缺乏鲁棒性，阻碍了其在安全至上的医疗应用中的使用。本研究聚焦于提升模型在多种因MR数据采集复杂性而表现出的变化伪影下的泛化性能。实验观察发现，批归一化（Batch Normalization）——一种医学图像分析深度学习模型训练中广泛使用的技术——是导致模型在变化环境中性能下降的关键因素。作为解决方案，我们建议采用组归一化（Group Normalization）和层归一化（Layer Normalization）等其他归一化技术，以增强模型对多样化图像伪影的鲁棒性。通过系统实验，我们证明组归一化和层归一化在处理多种MR伪影及分布偏移时能提供更高的准确率。