Predicting Amyloid Burden Using a Masked Multimodal‐Multitask Deep Learning Framework with Latent Diffusion‐based Synthetic PET

By reducing reliance on cost-intensive PET scans and handling missing data more effectively, our framework could substantially broaden access to early AD screening in diverse clinical settings. Future work will involve testing in larger, more varied cohorts, applying different tracers, and further evaluate its real-world impact on AD management.

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Publication Details

Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70856_097276
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting Amyloid Burden Using a Masked Multimodal‐Multitask Deep Learning Framework with Latent Diffusion‐based Synthetic PET

Seungjun Lee, Geon‐Ho Jahng, Seung Hyun Lee
Alzheimer s & Dementia
Dementia and Cognitive Impairment Research
article

Predicting Amyloid Burden Using a Masked Multimodal‐Multitask Deep Learning Framework with Latent Diffusion‐based Synthetic PET

Seungjun Lee, Geon‐Ho Jahng, Seung Hyun Lee
article en

Abstract

By reducing reliance on cost-intensive PET scans and handling missing data more effectively, our framework could substantially broaden access to early AD screening in diverse clinical settings. Future work will involve testing in larger, more varied cohorts, applying different tracers, and further evaluate its real-world impact on AD management.

Alzheimer s & DementiaVol. 21(S2)
Kyung Hee University (KR), Seoil University (KR), Kyung Hee University Hospital at Gangdong (KR)
Openalex Percentile: Top 35%
Dementia and Cognitive Impairment Research
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