arXiv Computer Vision
Aug 21

4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

arXiv:2604. 22700v2 Announce Type: replace Abstract: Modeling and predicting neurodegenerative disease progression from medical images remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning.

By Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh, Qingyu Zhao, Miaomiao Zhang
arXiv Machine Learning
Jun 18

Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Masks

arXiv:2606. 18354v1 Announce Type: cross Abstract: Recent advances in generative machine learning models have significantly improved medical imaging, offering promising solutions for data augmentation, privacy preservation, and improved model generalization.

By Muge Zhang, Muhammad Ali Khaliq, Jamal Alsakran, Byeong Kil Lee, Jeeho Ryoo
arXiv AI
Oct 15

Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention

The paper introduces a multimodal latent diffusion model that jointly synthesizes volumetric MRI scans and tabular clinical data within a shared latent space using cross‑attention. By fusing the modalities through a variational autoencoder before diffusion, the model reconstructs MRI and tabular data with separate decoders, producing anatomically plausible images that align with synthesized clinical attributes. Evaluation on the German National Cohort dataset shows high‑fidelity image generation and superior tabular performance compared to CTGAN, marking the first demonstration of joint MRI‑tabular modeling in a single diffusion framework.

By Daniel Mensing, Jan Kapar, Jochen G. Hirsch, Matthias G\"unther, Horst Hahn, Marvin N. Wright
arXiv Computer Vision
Sep 14

A Multimodal Explainable Deep Learning Framework for Alzheimer's Disease Diagnosis using 3D Magnetic Resonance Imaging and Clinical Data

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