Aug 2026· Journal of imaging informatics in medicine· 0 citations· 34 references
Medicine
TL;DR
The proposed GBP-SCMF provides an effective and interpretable multimodal strategy for computer-aided AD diagnosis and integrates three complementary mechanisms: generative brain-prior enhancement to inject disease-related pathological knowledge into imaging representations, self-regulated multimodal alignment to reduce modality bias and feature redundancy, and clinical-imaging collaborative fusion.
A multimodal deep learning classification framework integrating structural magnetic resonance imaging (sMRI) and clinical features and an adaptive gated fusion module that dynamically integrates concatenated global multimodal features with cross-modal interaction features is proposed.
Xiao-Li Yang, Chen-Chen Wang, Xiao Li et al.· Biomedical engineering and p...· 0 citations
Dementia is a major and growing global health burden, with Alzheimer's disease (AD) accounting for most cases. Timely and accurate diagnosis is central to managing this burden and increasingly depends on integrating complementary clinical and imaging information. Multimodal deep learning can combine these modalities fo...
Yusuf Brima, M. Atemkeng, L. Namamula et al.· 0 citations
Alzheimer's disease (AD) is a chronic neurodegenerative disease in which early diagnosis is crucial for early intervention and the appropriate management of patients. But the patho-octical changes in early stages, such as mild cognitive impairment (MCI), have been hard to detect with traditional diagnostic methods. Thi...
Tarun Kumar· Natural Resources for Human...· 0 citations
Alzheimer’s disease (AD) is a progressive neurode-generative disorder for which early and accurate diagnosis is critical for clinical management. Effective diagnosis depends on both disease staging and continuous cognitive monitoring through the Mini-Mental State Examination (MMSE), yet most existing deep learning-base...
Minh-Toan Le, Nhu-Y Tran-Van· International Conference on...· 0 citations
R-GenIMA, an interpretable multimodal large language model that pairs a region-of-interest vision transformer with genetic prompting to jointly analyze structural MRI and single nucleotide polymorphisms, shows that interpretable multimodal AI can integrate imaging and genetics to reveal disease mechanisms.
Kun Zhao, Si-Yuan Dai, Ying-Ying Zhang et al.· Frontiers in Radiology· 0 citations
An advanced DL-powered framework for the early prediction of AD using MRI data demonstrates high accuracy, robustness, and interpretability, supporting its potential for reliable early AD diagnosis in clinical settings.
K. V. S. V. Trinadh Reddy, Gundeboyina Srinivasalu, T. V. Rao et al.· Archives of Medical Research· 0 citations
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