Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation

TL;DR

This paper introduces $ abla$-LFM, a novel framework that models disease progression as a velocity field using flow matching, creating interpretable, patient-specific latent trajectories aligned with disease severity, validated on MRI data.

Contribution

It proposes a new method combining flow matching with patient-specific latent alignment to better model and interpret disease dynamics in longitudinal imaging.

Findings

Strong empirical performance on MRI benchmarks

Latent trajectories align with disease severity indicators

Provides interpretable visualization of disease progression

Abstract

Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment. While recent generative approaches have attempted to model progression, key mismatches remain: disease dynamics are inherently continuous and monotonic, yet latent representations are often scattered, lacking semantic structure, and diffusion-based models disrupt continuity with random denoising process. In this work, we propose to treat the disease dynamic as a velocity field and leverage Flow Matching (FM) to align the temporal evolution of patient data. Unlike prior methods, it captures the intrinsic dynamic of disease, making the progression more interpretable. However, a key challenge remains: in latent space, Auto-Encoders (AEs) do not guarantee alignment across patients or correlation with clinical-severity indicators (e.g., age and disease conditions). To address this, we propose to learn patient-specific latent alignment, which enforces patient trajectories to lie along a specific axis, with magnitude increasing monotonically with disease severity. This leads to a consistent and semantically meaningful latent space. Together, we present $\Delta$-LFM, a framework for modeling patient-specific latent progression with flow matching. Across three longitudinal MRI benchmarks, $\Delta$-LFM demonstrates strong empirical performance and, more importantly, offers a new framework for interpreting and visualizing disease dynamics.