Normative Modeling for AD Diagnosis and Biomarker Identification

TL;DR

This paper presents a novel end-to-end normative modeling approach using focal loss and adversarial autoencoders for improved Alzheimer's Disease diagnosis and biomarker discovery, demonstrating superior performance on large datasets.

Contribution

Introduces FAAE, an innovative model combining focal loss and adversarial autoencoders for AD diagnosis and biomarker identification, advancing current methodologies.

Findings

Outperforms previous state-of-the-art methods on OASIS-3 and ADNI datasets.

Effectively captures complex and challenging AD cases.

Provides enhanced insights into AD biomarkers.

Abstract

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}.