← Back to all news
arXiv Computer Vision By Yuxiang Feng, Juncheng Wang, Chao Xu, Wenlong Hou, Huihan Wang, Yijie Qian, Yang Liu, Baigui Sun, Yong Liu, Shujun Wang

Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Sep 15

Reverse Spatio-Temporal Disease Progression Modelling

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
diffusionbenchmarks
More like this →
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
diffusioncomputer-visionbenchmarkssafety
More like this →
arXiv Computer Vision
Oct 12

Stochastic Finite-Transition Learning and Exploration for Accelerated MRI Reconstruction

The paper introduces FLEX‑MRI, a method that accelerates MRI reconstruction by partitioning the recovery process into a few finite intervals defined by progressive k‑space support changes. Instead of learning fine‑grained local dynamics, FLEX‑MRI directly learns the reconstruction change across each interval and employs stochastic exploration during inference to sample alternative transition paths. Experiments on multi‑coil MRI datasets demonstrate that FLEX‑MRI outperforms competing methods while keeping computational efficiency comparable to few‑step baselines.

By Muhammad Usama Mirza, Esin \"Ozt\"urk I\c{s}{\i}k, Tolga \c{C}ukur
diffusion
More like this →