Normative Modeling for AD Diagnosis and Biomarker Identification
This work addresses Alzheimer's Disease diagnosis and biomarker identification for medical applications, representing an incremental improvement over existing methods.
The paper tackles Alzheimer's Disease diagnosis and biomarker identification by introducing a normative modeling approach with focal loss and adversarial autoencoders, which significantly outperforms previous state-of-the-art methods on OASIS-3 and ADNI datasets.
In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer's Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at \url{https://github.com/soz223/FAAE}.