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Novel Deep Learning Techniques for E...

Shah, Jay,

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Novel Deep Learning Techniques for Early Detection of Neurological Disorders /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Novel Deep Learning Techniques for Early Detection of Neurological Disorders // Jay Shah.
Author:	Shah, Jay,
Description:	1 electronic resource (167 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
Contained By:	Dissertations Abstracts International87-01B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32118911
ISBN:	9798288831812

Novel Deep Learning Techniques for Early Detection of Neurological Disorders /
Shah, Jay,

Novel Deep Learning Techniques for Early Detection of Neurological Disorders /Jay Shah. - 1 electronic resource (167 pages)

Source: Dissertations Abstracts International, Volume: 87-01, Section: B.

Early detection is critical in neurological disorders such as Alzheimer's disease and headaches, offering a window for interventions that may delay progression and improve outcomes. However, traditional diagnostic approaches often fail to provide timely and accurate assessments due to the multifactorial nature of these conditions. Multimodal neuroimaging, particularly magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary structural and functional information that can enhance diagnostic accuracy if leveraged effectively. This dissertation presents a three-step deep learning framework that addresses key challenges in medical imaging analysis. First, a robust brain age prediction model is introduced that reformulates age estimation as an ordinal classification task. This mitigates systematic bias in regression-based methods and better captures deviations from normative aging, critical for identifying early signs of neurological disorders. Building on this, the second chapter proposes a novel quantization technique to improve latent representation learning in medical imaging. It leverages Mahalanobis-distance-based soft assignment to multiple codebook vectors, addressing limitations of traditional vector quantization such as codebook collapse and poor reconstruction diversity. When integrated into downstream tasks such as brain age prediction, the proposed quantization method maintains performance in low-resource settings. The final chapter focuses on enhancing early prognosis through a multimodal generative approach. Specifically, an MRI-guided latent diffusion model is developed to super-resolve low-resolution PET scans. By conditioning the diffusion process on high-resolution anatomical MRI, the model improves spatial resolution and quantification of pathological biomarkers such as beta-amyloid. Collectively, these contributions form a clinically motivated, resource-efficient, and multimodal deep learning pipeline for advancing the early detection of neurological disorders.

English

ISBN: 9798288831812Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computational medical imaging

Novel Deep Learning Techniques for Early Detection of Neurological Disorders /
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300 $a 1 electronic resource (167 pages)
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500 $a Advisors: Wu, Teresa; Li, Baoxin Committee members: Su, Yi; Yang, Yezhou; Wang, Yalin.
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520 $a Early detection is critical in neurological disorders such as Alzheimer's disease and headaches, offering a window for interventions that may delay progression and improve outcomes. However, traditional diagnostic approaches often fail to provide timely and accurate assessments due to the multifactorial nature of these conditions. Multimodal neuroimaging, particularly magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary structural and functional information that can enhance diagnostic accuracy if leveraged effectively. This dissertation presents a three-step deep learning framework that addresses key challenges in medical imaging analysis. First, a robust brain age prediction model is introduced that reformulates age estimation as an ordinal classification task. This mitigates systematic bias in regression-based methods and better captures deviations from normative aging, critical for identifying early signs of neurological disorders. Building on this, the second chapter proposes a novel quantization technique to improve latent representation learning in medical imaging. It leverages Mahalanobis-distance-based soft assignment to multiple codebook vectors, addressing limitations of traditional vector quantization such as codebook collapse and poor reconstruction diversity. When integrated into downstream tasks such as brain age prediction, the proposed quantization method maintains performance in low-resource settings. The final chapter focuses on enhancing early prognosis through a multimodal generative approach. Specifically, an MRI-guided latent diffusion model is developed to super-resolve low-resolution PET scans. By conditioning the diffusion process on high-resolution anatomical MRI, the model improves spatial resolution and quantification of pathological biomarkers such as beta-amyloid. Collectively, these contributions form a clinically motivated, resource-efficient, and multimodal deep learning pipeline for advancing the early detection of neurological disorders.
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650 4 $a Medical imaging. $3 3172799
650 4 $a Neurosciences. $3 588700
650 4 $a Aging. $3 543123
653 $a Computational medical imaging
653 $a Computer vision
653 $a Deep learning
653 $a Machine learning
653 $a Neurological disorders
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690 $a 0800
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710 2 $a Arizona State University. $b Computer Science. $e degree granting institution. $3 3815405
720 1 $a Wu, Teresa $e degree supervisor.
720 1 $a Li, Baoxin $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 87-01B.
790 $a 0010
791 $a Ph.D.
792 $a 2025
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