Normative Modeling for AD Diagnosis and Biomarker Identification
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Abstract (arXiv)
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 urlhttps://github.com/soz223/FAAE.
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References · 6
- 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
- 2022Diffusion Models for Medical Anomaly Detectioncited by 232
- 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
- 2023FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoringcited by 4
- 2022Adversarial Focal Loss: Asking Your Discriminator for Hard Examplescited by 2
- 2022Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Diseasecited by 2
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