Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN
This work addresses the need for early and accurate visualization of Alzheimer's disease features in medical imaging, which is crucial for timely clinical intervention, though it appears incremental as it builds upon existing GAN-based methods.
The authors tackled the problem of visualizing morphological features for early-stage Alzheimer's disease diagnosis by proposing a Multidirectional Perception GAN (MP-GAN), which achieved superior performance on the ADNI dataset compared to existing methods, with results consistent with clinical observations.
The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for the early stages of AD is of great clinical value. In this work, a novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages. Specifically, by introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently. Thus, by utilizing the class-discriminative map from the generator, the proposed model can clearly delineate the subtle lesions via MR image transformations between the source domain and the pre-defined target domain. Besides, by integrating the adversarial loss, classification loss, cycle consistency loss and \emph{L}1 penalty, a single generator in MP-GAN can learn the class-discriminative maps for multiple-classes. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that MP-GAN achieves superior performance compared with the existing methods. The lesions visualized by MP-GAN are also consistent with what clinicians observe.