Application of Unsupervised Domain Adaptation for Structural MRI Analysis
This work addresses Alzheimer's disease diagnosis using medical imaging, but it is incremental as it builds on existing domain adaptation methods.
The study applied unsupervised domain adaptation to improve Alzheimer's disease detection from structural MRI data, achieving state-of-the-art performance in binary classification on the OASIS-1 dataset.
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.