The paper presents a multimodal 3D convolutional neural network that classifies Alzheimer’s disease using raw OASIS 1 MRI volumes. It fuses structural T1 images with gray matter, white matter, and cerebrospinal fluid probability maps to capture complementary neuroanatomical information. Evaluated with 5‑fold subject‑level cross‑validation, the model achieves a mean accuracy of 72.34 % and an ROC AUC of 0.7781, with GradCAM visualizations highlighting anatomically relevant regions such as the medial temporal lobe and ventricles.
By Md Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid, Abu Saleh Musa Miah, Najmul Hassan, Md Abdur Rahim, Jungpil Shin
The study presents an explainable multimodal deep‑learning framework that combines a 3D CNN for T1‑weighted MRI with a feedforward network for harmonized clinical and demographic data to diagnose Alzheimer’s disease. Using 6,479 ADNI records and 1,703 OASIS‑3 records, the authors compare various model configurations on three‑way and pairwise diagnostic tasks, finding that performance and explanations vary by task, modality, fusion strategy, and cohort. SHAP and Integrated Gradients consistently highlight the MMSE score as the most influential tabular feature, while CAM‑based explanations differ across model setups and cohorts, indicating that explainability is not a stable property under cohort shift.
By Yusuf Brima, Marcellin Atemkeng, Lakshmana Rao Namamula, Antoine Vacavant
Brain-PACE is a deep Siamese MRI framework that directly estimates the pace of structural brain ageing from paired T1‑weighted MRI scans, extending the LILAC model with spatial attention, soft label distribution learning, and a Cramér distance objective. In a study of participants with mild cognitive impairment, 42.6 % showed accelerated ageing, and faster Brain‑PACE scores correlated with greater functional and cognitive impairment as well as higher regional tau burden in key brain regions. The method improves probabilistic performance, reduces prediction bias, and provides predictive uncertainty, offering a complementary longitudinal imaging phenotype sensitive to early neurodegeneration.
By Samuel Maddox (School of Computing Sciences, University of East Anglia), Jacob Newman (School of Computing Sciences, University of East Anglia), Saber Sami (Norwich Medical School, University of East Anglia), Michal Mackiewicz (School of Computing Sciences, University of East Anglia), for the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers, Lifestyle flagship study of ageing
arXiv:2609.15888v1 Announce Type: cross
Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...
By Paul-Gabriel Nicolae, Irina Georgiana Mocanu
Deep learning models for neuroimaging have largely been developed for individual tasks, limiting knowledge transfer across applications. Here we introduce GenFAR, a modular deep learning framework that learns general, clinically informed features from brain MRIs.
BrainNorm is a normative foundation model trained on about 66,000 T1-weighted structural MRI scans. It uses language‑image style contrastive pretraining to create a Semantic Atlas Latent space (SAL), where each scan is encoded as atlas‑parcel embeddings that capture healthy aging trajectories. The model supports age‑consistent template matching, localized deviation scoring, and zero‑shot disease prediction, outperforming nine baselines across multiple downstream tasks and aligning its deviation patterns with known neurodegeneration pathology.