关于自动MR图像诊断中归一化方案对输入分布偏移的敏感性与鲁棒性研究
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
AI总结：
针对MRI图像伪影导致的深度学习模型输入分布偏移问题，研究发现Batch Normalization是性能下降主因，提出采用Group Normalization和Layer Normalization提升模型对变化伪影的鲁棒性与诊断准确性。
摘要：
磁共振成像（MRI）被认为是医学影像的金标准，因为MRI流程重建的图像展现出极佳的软组织对比度，这反过来使人类放射科医生能够轻松识别许多病理。最近，深度学习（DL）模型在使用这些重建图像作为输入诊断多种疾病方面也取得了最先进的性能。然而，MRI流程内的图像重建过程需要使用复杂的硬件并调整大量扫描仪参数，极易受到各种形式噪声的影响，导致图像中出现任意伪影。此外，噪声分布并非平稳，会在单台机器内部、不同机器以及患者之间发生变化，导致图像内出现不同的伪影。不幸的是，DL模型对这些变化的伪影非常敏感，因为这会导致训练和测试阶段之间的输入数据分布发生变化。这些模型对变化伪影缺乏鲁棒性，阻碍了它们在对安全性要求极高的医疗应用中的使用。在这项工作中，我们致力于提高这些模型在存在由MR数据采集复杂性引起的多种变化伪影情况下的泛化性能。在我们的实验中，我们观察到Batch Normalization（批归一化）作为医学图像分析DL模型训练期间广泛使用的技术，是这些变化环境中性能下降的重要原因。作为解决方案，我们提出使用其他归一化技术，如Group Normalization（组归一化）和Layer Normalization（LN，层归一化），将鲁棒性注入模型性能以应对变化的图像伪影。通过一系列系统性实验，我们表明GN和LN针对各种MR伪影和分布偏移提供了更好的准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
