arXiv:2603. 05693v2 Announce Type: replace-cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines.
By Zahra Karimaghaloo, Dumitru Fetco, Haz-Edine Assemlal, Hassan Rivaz, Douglas L. Arnold
The paper introduces a reverse spatio‑temporal disease progression model that reconstructs unobserved healthier anatomy from later diseased scans. It employs a two‑stage architecture: a frozen 3D vector‑quantised autoencoder creates a discrete latent space, and a Neural ODE learns continuous‑time dynamics, with a recurrent encoder initializing the latent state from reverse‑ordered observations. Experiments on a synthetic Morpho‑MNIST benchmark and longitudinal Alzheimer’s MRIs show the model can recover unseen prior states and outperform baseline methods in predicting healthy trajectories.
By Ulugbek Shernazarov, Moucheng Xu, Inomjon Ramatov
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
The paper introduces Latent Drift, a generative forecasting framework that predicts slow-evolving neurodegenerative disease progression by learning changes in a compressed semantic representation rather than full-resolution anatomy. It addresses two failure modes—identity collapse and continuous interpolation trap—by removing pixel-level identity from the prediction target and applying Finite Scalar Quantization to suppress high-frequency nuisance fluctuations. Experiments on longitudinal 3D brain MRI demonstrate that Latent Drift outperforms diffusion and autoregressive transformer baselines in both generative fidelity and clinically relevant metrics.
By Yuxiang Feng, Juncheng Wang, Chao Xu, Wenlong Hou, Huihan Wang, Yijie Qian, Yang Liu, Baigui Sun, Yong Liu, Shujun Wang
We introduce SMART, a framework for learning a flexible, interpretable, and scalable spatio-temporal brain atlas from longitudinal high-resolution 3D medical images. Existing approaches to spatio-temporal atlas construction rely on black-box generative models that lack flexibility, limit interpretability, and struggle to scale to high-dimensional data.
arXiv:2606. 19651v1 Announce Type: new Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing.
By Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert