You Suhang, Reyes Mauricio
Medical Image Analysis Group, ARTORG, Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.
Front Neuroimaging. 2022 Oct 28;1:1012639. doi: 10.3389/fnimg.2022.1012639. eCollection 2022.
Contrast and texture modifications applied during training or test-time have recently shown promising results to enhance the generalization performance of deep learning segmentation methods in medical image analysis. However, a deeper understanding of this phenomenon has not been investigated. In this study, we investigated this phenomenon using a controlled experimental setting, using datasets from the Human Connectome Project and a large set of simulated MR protocols, in order to mitigate data confounders and investigate possible explanations as to why model performance changes when applying different levels of contrast and texture-based modifications. Our experiments confirm previous findings regarding the improved performance of models subjected to contrast and texture modifications employed during training and/or testing time, but further show the interplay when these operations are combined, as well as the regimes of model improvement/worsening across scanning parameters. Furthermore, our findings demonstrate a spatial attention shift phenomenon of trained models, occurring for different levels of model performance, and varying in relation to the type of applied image modification.
最近，在训练或测试时应用的对比度和纹理修改已显示出有望提高医学图像分析中深度学习分割方法的泛化性能。然而，尚未对这一现象进行更深入的研究。在本研究中，我们使用来自人类连接组计划的数据集和大量模拟MR协议，通过可控的实验设置来研究这一现象，以减轻数据混杂因素，并探究在应用不同程度的基于对比度和纹理的修改时模型性能发生变化的可能原因。我们的实验证实了之前关于在训练和/或测试时进行对比度和纹理修改的模型性能提高的发现，但进一步展示了这些操作组合时的相互作用，以及模型在不同扫描参数下性能改善/恶化的情况。此外，我们的研究结果表明，经过训练的模型存在空间注意力转移现象，该现象在不同的模型性能水平下都会出现，并且会因应用的图像修改类型而异。
