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REVIEW 4 major objections 5 minor 94 references

Improving Generalization of Medical Image Registration Foundation Model

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Training the uniGradICON medical image registration foundation model with Sharpness-Aware Minimization improves cross-dataset registration accuracy.

desk verdict Plausible SAM application to a registration foundation model, but the central 'significant improvement' claim rests on a boxplot with no statistics and a table that omits the primary baseline. read the letter →

arxiv 2505.06527 v1 pith:EZN4TKQV submitted 2025-05-10 cs.CV cs.AI

Jing Hu , Kaiwei Yu , Hongjiang Xian , Shu Hu , Xin Wang This is my paper · ORCID
classification cs.CVcs.AI
keywords Sharpness-AwareMinimization(SAM)medicalimageregistrationfoundationmodelgeneralizationcross-datasetdeformableuniGradICONlosslandscape
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper addresses a known weakness of medical image registration foundation models: they lose accuracy when applied to datasets, anatomies, or modalities they were not trained on. The authors propose training the uniGradICON foundation model with Sharpness-Aware Minimization (SAM), an optimizer that steers the model toward flat regions of the loss landscape, as a way to improve cross-dataset generalization. Their experiments on five held-out datasets — cardiac, liver, abdominal MR-CT, hippocampus, and lung CT — show that the SAM-trained model achieves consistently higher Dice overlap scores than the same model without SAM. The paper argues that this flat-minima training makes the model more robust across different anatomical structures and imaging conditions, and it visualizes a flatter loss landscape for the SAM-trained model in support.

What carries the argument

Sharpness-Aware Minimization (SAM) is the load-bearing mechanism: an optimization scheme that minimizes, not the training loss at the current parameters, but the worst-case loss in a small ball around them, by perturbing weights along the gradient direction by a radius ρ before computing each update. The paper applies SAM on top of the uniGradICON foundation model, whose architecture is built from ICON's downsampling (Down) and two-step (TS) operators and trained with the GradICON loss — a localized normalized cross-correlation similarity term plus a gradient inverse-consistency regularizer. SAM's adversarial perturbation is computed with the efficient first-order approximation of the inner maximization, so the extra cost per update is roughly one additional forward-backward pass. The flatness of the resulting loss landscape is the explanatory link the paper draws to generalization.

What would settle it

Retrain the baseline uniGradICON model from scratch on the same composite training dataset, using the same random seed, number of epochs, learning rate, and loss weights, with SAM as the only added component, and measure Dice overlap on the same five test datasets. If the Dice differences between the two models disappear or become statistically insignificant, the paper's central claim would be falsified.

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Extended reading notes

Core claim

The central claim is that incorporating Sharpness-Aware Minimization into the training of the uniGradICON foundation model improves its generalization across medical image registration tasks. SAM replaces the ordinary gradient update with a two-step procedure: first compute the gradient at the current weights, take a small step of size ρ in that gradient direction to find a perturbation that maximizes the training loss (an adversarial point within a neighborhood), then compute the actual update gradient at that perturbed point. This minimax objective biases training toward minima whose neighborhoods are uniformly low in loss — flat minima — which the paper finds produces a flatter loss landscape than the base model. The paper evaluates the approach on the ACDC cardiac and SLIVER liver datasets against four comparison baselines and against uniGradICON on five datasets, reporting that the SAM-trained model achieves the highest average Dice scores and near-zero or zero percentages of folded voxels.

Load-bearing premise

The claim that SAM alone causes the improvement rests on the assumption that the comparison uniGradICON model was trained under identical conditions, with the same data sampling, epochs, loss weights, and random seeds, so that adding SAM is the only change.

Editorial extensions

If this is right

  • The SAM-trained model reports higher average Dice scores than all compared baselines on the ACDC and SLIVER datasets, with folded-voxel percentages near or exactly zero.
  • Across the five ablation datasets, the method consistently achieves significantly higher Dice coefficients than uniGradICON, indicating stronger cross-dataset generalization.
  • The loss-landscape visualizations are flatter for the SAM-trained model, matching the proposed mechanism that flat minima generalize better.
  • Because SAM adds only one hyperparameter and is compatible with any first-order optimizer, the same recipe can be applied to other registration networks without architectural changes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A controlled re-training study with identical seeds and hyperparameters, varying only SAM, would isolate the optimizer's true contribution from any training-schedule differences in the published comparison.
  • The method's success on both MRI and CT across four anatomical regions suggests the flat-minima recipe is modality-agnostic and might transfer to 2D imaging (X-ray, ultrasound) with minimal adjustment.
  • Quantifying the sharpness of the found minimum directly — for instance, by measuring the curvature of the loss surface — rather than relying on visual loss-landscape plots would let future work verify the generalization mechanism more rigorously.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes incorporating Sharpness-Aware Minimization (SAM) into the uniGradICON medical image registration foundation model. The method replaces the base optimizer's gradient with one computed at an adversarial perturbation, supposedly leading to flatter minima and improved cross-dataset generalization. The authors train on a composite dataset of four sources (COPDGene, OAI, HCP, L2R-Abdomen) and evaluate on five test sets (ACDC, SLIVER, and three Learn2Reg tasks). The central claim, stated in Section IV-D, is that adding SAM to uniGradICON 'consistently achieves significantly higher Dice coefficients on all five datasets,' supported by a boxplot (Fig. 5) and a qualitative loss-landscape visualization (Fig. 6). Quantitative comparisons against other methods on ACDC and SLIVER are given in Table II.

Significance. If substantiated, the result would be of practical interest: a drop-in modification (SAM) to an existing foundation model that improves cross-dataset generalization with no architectural change is a useful contribution. The paper also makes the method reproducible by releasing code. However, the core evidence for the central claim is currently descriptive rather than statistical. The reported margins over the best runner-up in Table II are small (0.0038 on ACDC, 0.0042 on SLIVER), and the primary baseline uniGradICON is absent from that table. The paper's strength is its clear framing of a plausible hypothesis (flat minima improve registration generalization) and the use of established components (GradICON/uniGradICON, SAM). The weakness is that the direct head-to-head comparison is not quantitatively reported, so the central claim is not yet established.

major comments (4)
  1. [Section IV-D, Fig. 5] The claim that 'our method consistently achieves significantly higher Dice coefficients on all five datasets' is supported only by a boxplot. The figure reports no means, standard deviations, per-dataset sample sizes, or statistical tests. The word 'significantly' is therefore unsupported. Please provide the underlying numerical results (e.g., mean ± std for each dataset and each method) and, where appropriate, a paired significance test or confidence intervals, to substantiate the claim.
  2. [Section IV-C, Table II] The primary baseline uniGradICON is missing from Table II, the only quantitative comparison table. Without numbers for uniGradICON on ACDC and SLIVER, the reader cannot assess whether SAM improves over its base model in the two datasets that do have quantitative results. Please add uniGradICON results to Table II, or clearly state why they are omitted.
  3. [Section IV-D, Fig. 5] The paper does not state whether the uniGradICON baseline in Fig. 5 was retrained from scratch on the identical composite dataset with the same 800/200 epochs, loss weights (λ=1.5), learning rate (η=5e-5), random seeds, and sampling protocol, with SAM as the only difference. If the baseline numbers come from a published checkpoint or a different training procedure, the claimed generalization gain could be caused by training details rather than by SAM. Please specify the exact training setup for both models.
  4. [Section IV-D, Fig. 6] The loss-landscape visualization is qualitative and does not quantify flatness (e.g., via the trace of the Hessian, the largest eigenvalue, or a sharpness measure). As presented, Fig. 6 is suggestive but does not independently establish the mechanism of improved generalization. Please either add a quantitative flatness measure or temper the mechanistic claim.
minor comments (5)
  1. [Abstract] The abstract contains a malformed URL: 'https://github.com/Promise13/fm_sam}{https://github.com/Promise13/fm\_sam'. Please correct the formatting.
  2. [Section III-B] The section heading 'Taining loss' should be 'Training loss'.
  3. [Section II-D] The phrase 'this method has effectively improved model performance' is vague; please specify the tasks and the magnitude of improvements reported in prior work.
  4. [Section IV-A, Table I] The 'SLIVER' dataset is listed as MRI, but the cited SLIVER dataset (Heimann et al., 2009) is a CT dataset. Please verify the modality label.
  5. [Section IV-C] The boxplot in Fig. 3 is described in the text, but no caption appears in the manuscript body. Please add a complete caption for Fig. 3.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the core contribution is an empirical training modification (SAM on uniGradICON), evaluated against external baselines and the same base model; no derivation reduces to its own inputs.

full rationale

The paper's method is externally sourced and independently applied: the SAM objective (Eqs. 5-7) is taken from Foret et al. [64], the registration network and loss (Eq. 8) come from GradICON/uniGradICON [60,82], and the hyperparameters (rho=0.1, lambda=1.5, learning rate, epochs) are stated as fixed settings rather than fitted to the test results. The central comparison in Table II is against external baselines (Elastix, VoxelMorph, DiffuseMorph, TransMorph) on held-out ACDC and SLIVER data, and the ablation in Fig. 5 compares the SAM-trained model against the uniGradICON base model. No test-set values are folded back into the method, no parameter is defined in terms of the target metric, and no load-bearing argument reduces to a self-citation. The paper does have reporting weaknesses: the Fig. 5 ablation shows only boxplots without numbers or significance tests, and the text does not explicitly state that the uniGradICON baseline was retrained under identical conditions with SAM as the only difference. Those are evidence-quality and correctness-risk issues, not circularity: a weak empirical comparison is still an empirical comparison. Self-citations in the reference list concern prior medical-image-registration and deepfake/segmentation applications and are not used to justify the SAM-generalization claim. Accordingly, no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities, forces, or conserved quantities. Its free parameters are standard training hyperparameters with values inherited from prior work. The main non-standard assumption is that the uniGradICON baseline is trained identically except for SAM, which is not demonstrated.

free parameters (3)
  • sam_neighborhood_size_rho = 0.1
    SAM perturbation radius, set by hand following prior SAM practice; not tuned to the test sets.
  • inverse_consistency_weight_lambda = 1.5
    Weight for the GradICON inverse-consistency regularizer, taken from GradICON defaults.
  • learning_rate_eta = 5e-5
    Adam learning rate, taken from GradICON defaults; not fit to the data.
assumptions (4)
  • standard math SAM's minimax objective and first-order approximation (Eq. 5-7) are valid and improve flatness.
    Taken from Foret et al. [64]; the paper applies it without modification.
  • domain assumption Flat minima generalize better to new datasets.
    Imported from SAM literature; stated in Section II-D and used to interpret Fig. 6.
  • domain assumption The composite training set (COPDGene, OAI, HCP, L2R-Abdomen) is a sufficient basis for a registration foundation model.
    Same training set as uniGradICON [60]; no analysis of domain shift inside the training data.
  • ad hoc to paper The comparison baseline uniGradICON is trained under identical conditions except for SAM.
    Section IV-D presents the comparison without reporting baseline training details.

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Cite this review

Pith. "Pith review of Improving Generalization of Medical Image Registration Foundation Model." pith.science (2026). https://pith.science/paper/EZN4TKQV

@misc{pith2026250506527,
  author       = {Pith},
  title        = {Pith review of: Improving Generalization of Medical Image Registration Foundation Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EZN4TKQV}},
  note         = {Machine review of arXiv:2505.06527}
}
read the original abstract

Deformable registration is a fundamental task in medical image processing, aiming to achieve precise alignment by establishing nonlinear correspondences between images. Traditional methods offer good adaptability and interpretability but are limited by computational efficiency. Although deep learning approaches have significantly improved registration speed and accuracy, they often lack flexibility and generalizability across different datasets and tasks. In recent years, foundation models have emerged as a promising direction, leveraging large and diverse datasets to learn universal features and transformation patterns for image registration, thus demonstrating strong cross-task transferability. However, these models still face challenges in generalization and robustness when encountering novel anatomical structures, varying imaging conditions, or unseen modalities. To address these limitations, this paper incorporates Sharpness-Aware Minimization (SAM) into foundation models to enhance their generalization and robustness in medical image registration. By optimizing the flatness of the loss landscape, SAM improves model stability across diverse data distributions and strengthens its ability to handle complex clinical scenarios. Experimental results show that foundation models integrated with SAM achieve significant improvements in cross-dataset registration performance, offering new insights for the advancement of medical image registration technology. Our code is available at https://github.com/Promise13/fm_sam}{https://github.com/Promise13/fm\_sam.

Figures

Figures reproduced from arXiv: 2505.06527 by the authors.

Figure 2
Figure 2. Schematic of the SAM parameter update. Algorithm 1 SAM Algorithm. Input: Training set S △ = Un i=1 {(xi , yi)} ,loss function L : W × X × Y −→ R+ ,batch size b,step size η > 0, neighborhood size ρ > 0 (default 0.15) Output: Model trained with SAM Initialize weights w0, t = 0; while not converged do Sample batch B = {(x1, y1, ...(xb, yb))}; Compute gradient▽wLB (w) of the batch’s training loss; Compute ϵˆ(w) per equa… view at source ↗
Figure 4
Figure 4. An example of axial slices for cardiac MR image registration, [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Loss landscape visualization. Left: uniGradICON. Right: Ours. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.