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MUMINS: Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis
Anna Oliveras, Roger Marí, Rafael Redondo, Oriol Guardià, Cynthia Ifeyinwa Ugwu, Ana Tost, Bhalaji Nagarajan, Carolina Migliorelli, Vicent Ribas, Petia Radeva
TL;DR
Forecasting patient-specific anatomical change is difficult because evolution is subtle, stochastic, and difficult to represent with deterministic models or costly multi-pass uncertainty estimation. MUMINS jointly diffuses baseline and residual information while predicting a spatial uncertainty map in one reverse process, using metadata conditioning and baseline reinjection. After dataset-specific retraining, it matches or outperforms domain-specific methods on lung CT and brain MRI, with limitations in variance-propagation assumptions and uncertainty localization.
Problem
Anatomical progression forecasting must represent subtle, patient-specific, stochastic changes while preserving static anatomy and estimating uncertainty efficiently.
Method
MUMINS jointly denoises a baseline scan and follow-up residual, reinjects the baseline during sampling, conditions on metadata, and predicts voxel-wise uncertainty in one reverse process.
Results
MUMINS matches or outperforms domain-specific state-of-the-art methods on PNG lung CT and OASIS-3 brain MRI after dataset-specific retraining.
Takeaways & Limitations
A universal architecture can support follow-up synthesis and inexpensive single-pass uncertainty estimation across lung CT and brain MRI datasets.
Takeaways & Limitations
Inference-time variance propagation assumes mutually independent diagonal components and omits the denoiser Jacobian because 3D backward passes are infeasible at scale.
Abstract
from arXiv · showhide
Forecasting anatomical changes such as tumor growth and neurodegeneration is a challenging generative vision task. Morphological evolution is subtle relative to static anatomy, highly patient-specific, and inherently stochastic. Existing methods struggle with several issues: deterministic networks ignore biological stochasticity, while standard diffusion models require computationally prohibitive multi-pass sampling to quantify uncertainty. We propose MUMINS (Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis), an efficient diffusion framework that jointly diffuses a baseline scan and its follow-up residual, summed to synthesize the follow-up scan, while concurrently predicting a spatial uncertainty map, in a single reverse diffusion process. Conditioned on the time interval and relevant metadata, it preserves fine-grained anatomy by dynamically re-injecting the baseline as a soft anchor at every denoising step, and a negative-log-likelihood head learns the uncertainty map to explicitly flag error-prone regions. Designed without organ-specific heuristics, the same architecture is reused across anatomies via separate, dataset-specific retraining. Extensive evaluations demonstrate that dataset-specific retraining of MUMINS matches or outperforms dedicated, domain-specific state-of-the-art methods on lung CT (PNG) and brain MRI (OASIS-3). Project page: https://github.com/aolivtous/MUMINS.
1. Introduction
MUMINS addresses the stochastic, patient-specific difficulty of forecasting anatomical change while preserving baseline anatomy and estimating spatial uncertainty. Its universal diffusion design matches or outperforms domain-specific methods after dataset-specific retraining.
- The clinical motivation spans lung-nodule assessment and longitudinal neurodegeneration tracking, where future scans at arbitrary intervals support prognostic decisions.
- Forecasting is difficult because morphological change is small relative to static anatomy, patient-specific, and inherently stochastic, requiring plausible changes alongside preserved baseline details.
- Existing methods remain limited by deterministic single-future predictions, organ-specific components, and costly multi-pass uncertainty estimation.
- MUMINS jointly denoises the baseline and baseline-to-follow-up residual, reinjecting the baseline during inference to preserve anatomy while synthesizing morphological change.
- A single reverse-diffusion process predicts voxel-wise uncertainty through likelihood-based learning, avoiding Monte Carlo’s repeated inferences per sample.
- MUMINS matches or outperforms domain-specific state-of-the-art methods on PNG lung CT and OASIS-3 brain MRI after dataset-specific retraining.
2. Related Work
Longitudinal medical image progression methods seek to forecast patient-specific anatomical change and quantify uncertainty. Existing deterministic and diffusion-based approaches leave gaps in modeling plausible follow-ups and efficiently estimating uncertainty.
- Longitudinal progression forecasting targets patient-specific anatomical change from baseline scans, including pulmonary nodule evolution and neurodegenerative brain progression.
- Deterministic and registration-based methods: Deterministic and registration-based models learn fixed baseline-to-follow-up transitions, including voxel warping and intensity refinement for lung nodules.
- Diffusion-based progression: Diffusion models can generate high-fidelity follow-up images, but recent progression methods remain strongly anatomy-, disease-, or modality-specific.
- Monte Carlo uncertainty estimation requires N × T network evaluations per volume, while BayesDiff still resamples weights at every step and retains the multi-pass bottleneck.
3. Methodology
MUMINS jointly models baseline anatomy and longitudinal residuals in a metadata-conditioned 3D diffusion process, producing follow-up scans and voxel-wise uncertainty. Baseline re-injection anchors synthesis to patient anatomy, while variance propagation and NLL training make single-pass uncertainty estimation tractable.
- Longitudinal formulation: MUMINS jointly denoises the baseline and follow-up residual, then adds the denoised residual to the baseline to synthesize the future scan.The residual formulation separates static anatomy from longitudinal change.
- Diffusion model: A 3D U-Net predicts diffusion noise and per-voxel standard deviation for the joint baseline-residual state.The variance head supports uncertainty learning alongside noise prediction.
- Temporal and metadata conditioning: Conditioning combines the inter-scan interval, baseline age, and per-axis blur changes, with Δb = 0 as the information-respecting forecasting setting.The oracle Δb = GT setting is reported only as a diagnostic ceiling.
- Hybrid training objective: The hybrid objective combines joint noise reconstruction with an NLL uncertainty term, using stop-gradient decoupling and λ_NLL = 0.01.This prevents uncertainty calibration from reducing NLL by inflating predicted standard deviations at the expense of denoising.
- Baseline injection: At every reverse step, MUMINS overwrites the baseline channel with a forward-marginal sample, using one fixed noise realization to maintain a coherent anatomical trajectory.The residual is thereby guided by temporally consistent baseline context; single-trajectory injection is preferred to per-step resampling in the cited ablation.
- Uncertainty propagation: Variance propagation uses diagonal covariance and J_t = 0 approximations, with delayed activation selected by NLL to avoid monotonically accumulating over-dispersed uncertainty.Stochastic sampler noise is required for both calibrated uncertainty and reconstruction quality in the reported ablation.
4. Experiments and Results
MUMINS is evaluated on lung CT and brain MRI for synthesis quality, uncertainty estimation, qualitative fidelity, and component contributions. It performs strongly against organ-specific baselines while its uncertainty estimates provide useful error localization and calibration at substantially lower sampling cost.
- Experimental setup: The experiments use PNG lung CT and OASIS-3 brain MRI, with metrics averaged over longitudinal pairs and synthesis assessed by MAE, PSNR, and SSIM.Uncertainty is evaluated against voxel-wise absolute error for both whole-image and relevant ROI settings.
- Synthesis results: MUMINS achieves the best aggregate whole-image lung metrics and leads aggregate nodule-ROI PSNR, while brain performance is ahead of TADM-3D and BrLP on whole-image metrics.On PNG, whole-image PSNR is 23.53 versus 22.85 dB for NGP-Net, and nodule-ROI PSNR is 24.89 dB; on OASIS-3, MUMINS is ahead of TADM-3D and BrLP on all three whole-image metrics.
- Qualitative results: Qualitatively, MUMINS produces sparse, low-amplitude lung errors and clean brain reconstructions, whereas competing methods show broader errors or blurrier follow-ups.On lung CT, little error appears over the nodule; on brain MRI, MUMINS is described as cleanest overall while BrLP is visibly blurrier.
- Uncertainty evaluation: MUMINS uncertainty localizes lung error with ρ = 0.61 and PICP95 = 0.91, while brain ROI uncertainty remains better calibrated than the 20-seed MC reference.For brain ROI, single-pass uncertainty has ρ = 0.26 versus 0.32 for MC, but PICP95 = 0.97 versus 0.59; cross-estimator agreement is 0.79 for lung and 0.65 for brain ROI.
- Uncertainty evaluation: The single-pass sparsification curve approaches oracle behavior as high-uncertainty voxels are removed, though it remains above the 20-seed MC curve.The higher single-pass AUSE is consistent with weaker sparsification than MC, while both curves show monotonically decreasing MAE.
- Ablation studies: Baseline reinjection produces the largest ablation gains, increasing PSNR from 21.00 to 23.73 dB and SSIM from 56.27% to 72.38%.Residual prediction reduces PSNR standard deviation from 4.09 to 1.65 dB, blur conditioning raises PSNR from 17.40 to 20.69 dB, and uncertainty prediction adds a further gain to 21.00 dB.
5. Conclusions
MUMINS synthesizes follow-up scans and voxel-wise uncertainty in one reverse diffusion process, using joint baseline-residual modeling and baseline re-injection to preserve anatomy. It matches or outperforms domain-specific baselines on lung CT and brain MRI, while retaining limitations in brain localization and cross-horizon consistency.
- MUMINS matches or outperforms domain-specific baselines on lung CT and brain MRI, with single-pass uncertainty correlating cheaply with the Monte Carlo reference.The framework uses no organ-specific components, but brain uncertainty localization trails Monte Carlo on near-saturated targets.
- MUMINS synthesizes a follow-up scan and voxel-wise uncertainty map in a single reverse diffusion process.Its joint baseline-residual state and per-step baseline re-injection preserve fine anatomy while generating plausible change.
- The likelihood-based variance head provides uncertainty without Monte Carlo’s costlier repeated sampling.This enables single-pass uncertainty estimation while retaining correlation with the Monte Carlo reference.
- Predictions queried at different future intervals from the same baseline are generated independently, without explicit cross-horizon consistency.The authors identify this as a remaining limitation and propose extending the framework to further organs and modalities.