论文标题
无监督的域适应性用于结构MRI分析
Application of Unsupervised Domain Adaptation for Structural MRI Analysis
论文作者
论文摘要
这项工作的主要目的是研究在阿尔茨海默氏病（AD）检测到OASIS数据集中的各种应用中，无监督的域适应方法的有效性。我们还探索图像重建和图像合成，用于分析和生成3D结构MRI数据，以建立用于异常检测的性能基准。我们成功地证明，在监督和无监督的设置中实施时，域的适应性可改善AD检测的性能。此外，所提出的方法还达到了OASIS-1数据集上二进制分类的最新性能。
The primary goal of this work is to study the effectiveness of an unsupervised domain adaptation approach for various applications such as binary classification and anomaly detection in the context of Alzheimer's disease (AD) detection for the OASIS datasets. We also explore image reconstruction and image synthesis for analyzing and generating 3D structural MRI data to establish performance benchmarks for anomaly detection. We successfully demonstrate that domain adaptation improves the performance of AD detection when implemented in both supervised and unsupervised settings. Additionally, the proposed methodology achieves state-of-the-art performance for binary classification on the OASIS-1 dataset.
