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REVIEW 3 major objections 6 minor 50 references

CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Self-correcting SAM improves medical image segmentation on unseen domains.

desk verdict The self-correcting SAM idea is promising and the experiments are solid, but a training-input/supervision mismatch in the error decoder (likely a typo) needs to be fixed before the central claim holds. read the letter →

arxiv 2411.10136 v1 pith:EM4N2GYT submitted 2024-11-15 cs.CV

Yihang Fu , Ziyang Chen , Yiwen Ye , Xingliang Lei , Zhisong Wang , Yong Xia This is my paper · ORCID
classification cs.CV
keywords domaingeneralizationmedicalimagesegmentationSegmentAnythingModelself-correctingerrordecoderprompt-basedrefinementprostateMRIopticdiscandcup
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

CoSAM makes the case that a Segment Anything Model (SAM) can improve cross-domain medical image segmentation by correcting its own outputs rather than relying on a learned prompt generator. The paper's proposal is to generate a coarse mask from SAM without prompts, train a generalized error decoder to mark which pixels of that mask are wrong, flip those pixels to produce a corrected mask, and then use point, box, and mask prompts derived from the corrected mask to prompt SAM again in an iterative loop. Under leave-one-domain-out training on prostate MRI (six domains) and optic disc and cup segmentation from fundus photos (four domains), the paper reports average Dice scores of 78.41 and 68.57, above five SAM-based baselines. A sympathetic reader would read the contribution as a mechanism for making SAM's prompt-based advantage available automatically at test time, without manual prompts.

What carries the argument

The load-bearing component is the generalized error decoder, a U-Net decoder that takes the concatenation of SAM's image embedding and mask embeddings from SAM's dense prompt encoder, which is reused as a mask encoder, and predicts an error map in which correct pixels are 0 and error pixels are 1. It is trained with random Bernoulli flips of the binary coarse mask and a class-balanced weighted loss, which is meant to simulate distribution shift and clinician correction. The self-correcting loop, driven by corrected masks and diverse point, box, and mask prompts, is what turns the predicted error map into improved segmentation.

What would settle it

Take a target-domain image whose scanner or protocol never appears in training, compute CoSAM's predicted error map, and compare it with the true error map defined as the XOR of the coarse mask and the clinician label; if the self-correction loop's improvement over the coarse mask disappears whenever the predicted error map misses a large fraction of true error pixels, the core claim fails.

Watch

Extended reading notes

Core claim

CoSAM claims that coarse masks produced by a prompt-free SAM are a sufficient starting point for prompting, provided the model can learn to correct them. During training, random Bernoulli perturbations of the coarse mask make the error decoder more robust to distribution shifts in the error-prediction task. At inference, corrected masks are converted into three prompt types—top-K point prompts from error locations, a box prompt from the largest foreground-connected region, and a mask prompt from the corrected mask—and fed back into SAM. The loop repeats for up to T iterations and stops early when the predicted number of error points stops falling. CoSAM reports that this loop outperforms SAM and five SAM-based baselines on both benchmark tasks.

Load-bearing premise

The load-bearing premise is that an error decoder trained on source images, with random flips applied only to the binary mask and never to image appearance, will predict which pixels of the coarse mask are wrong accurately enough on an unseen target domain to drive useful corrections.

Editorial extensions

If this is right

  • A test-time pipeline can avoid manual prompts and separate trainable prompt generators, using SAM's own coarse mask as the seed.
  • Predicted error maps can serve as a quality monitor, since the refinement loop stops when the estimated number of error points would increase.
  • Combining point, box, and mask prompts gives more consistent gains than any single prompt type in the reported ablations.
  • On the two benchmarks, the improvement is concentrated in average performance across leave-one-domain-out settings rather than in every individual source domain.

Reading between the lines

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

  • Nothing in the method is organ-specific, so the same correct-then-reprompt cycle should transfer to other promptable segmentation backbones and other 2D medical targets, a claim the paper leaves untested.
  • Because the perturbation used to train the error decoder only flips mask pixels, real scanner-level appearance shifts remain the likeliest failure mode; adding image-level augmentations on top of the mask perturbation would be a direct test of that boundary.
  • The early-stop rule could be reinterpreted as a confidence signal and used to reject unreliable corrections rather than simply halting refinement, an extension the paper does not explore.
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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

3 major / 6 minor

Summary. The paper proposes CoSAM, a SAM-based method for domain generalization in 2D medical image segmentation. CoSAM first produces a coarse mask with SAM in a prompt-free manner, then uses a learned error decoder to predict an error map between the coarse mask and the ground truth, and corrects the mask accordingly. The corrected mask and a set of diverse prompts (point, box, mask) are fed back into SAM's mask decoder, and this refinement is repeated for T iterations with an early-stop mechanism. The method is evaluated with leave-one-domain-out experiments on prostate MRI segmentation (six domains) and joint optic disc/cup segmentation (four domains), reporting average DSC improvements over five SAM-based baselines.

Significance. If the reported results are reproducible, CoSAM contributes a conceptually simple way to leverage SAM's prompt-based design without requiring a prompt generator or manual prompts, addressing a real need in clinical deployment under distribution shifts. The use of a dedicated error decoder to guide iterative refinement is a sensible idea, and the reported improvements over multiple baselines on two benchmarks are encouraging. However, the verification is weakened by the absence of statistical significance tests and by an internal inconsistency in the error-decoder training objective described below.

major comments (3)
  1. [Section 4.3, Eq. (4)-(5), Algorithm 1 lines 7-9] The error decoder receives the perturbed mask ˆyp as input but is supervised with the error map e = XOR(ˆy, y) of the unperturbed coarse mask. At inference (Algorithm 2, line 7) the decoder receives the unperturbed current mask and is expected to predict its error map. As written, the training objective teaches the decoder to produce error maps for a mask it does not see, so the decoder cannot learn the conditional mapping required for self-correction. This internal inconsistency undermines the attribution of the Le contribution in Table 3 and makes the method unreproducible. The authors likely meant to compute e = XOR(ˆyp, y) or to feed the unperturbed mask in Eq. (4); either way the manuscript must be corrected and the experiments rerun.
  2. [Tables 1-5] No standard deviations, confidence intervals, or significance tests are reported for any DSC value, and the reported improvements over the best baselines are modest (average DSC gains of 1.64 and 1.72 points on the two tasks). Since the main claim is superiority over existing methods, the absence of repeated-run statistics makes it difficult to judge whether the improvements are stable. Please report mean and standard deviation over at least three runs, and ideally paired significance tests.
  3. [Section 4.3, Eq. (3)] The perturbation used to train the error decoder acts only on the binary mask, not on the image appearance. Because the decoder also receives image embeddings E(x) as input, the domain shift in image statistics (scanner/protocol variations) is not simulated, so the claim that the decoder is 'generalized' across image domains is not directly supported. The authors should provide evidence that the error decoder maintains accuracy on target domains, for example by reporting error-map quality or by ablating the perturbation under image-level augmentation.
minor comments (6)
  1. [Eq. (5)] The weighted BCE is written as Le(˜e, e) = ωe log(˜e) + (1− e) log(1− ˜e), which is ambiguous about whether ω multiplies e and where the minus sign is. Please write the standard form Le = −[ω e log(˜e) + (1−e) log(1−˜e)].
  2. [Section 5.3] The baseline 'SAM' is described as 'fine-tuning SAM with the full image size as box prompts'; this conflicts with the earlier description of prompt-free methods. Please clarify the exact prompting configuration of the SAM baseline.
  3. [Table 5] The rows for Random-K and Top-K contain formatting artifacts (font-size shift) in the submitted PDF; please ensure the table renders uniformly.
  4. [References] References [5] and [6] are the same paper (Chen et al., 'Treasure in distribution...'); duplicate bibliography entries should be merged.
  5. [Section 4.3, text after Eq. (6)] The definition of nw as 'the number of correct and error points in ˜e' is contradictory; from the equation nw should refer only to error points and nr to correct points. Please fix the wording.
  6. [Section 4.5 and Algorithm 2] The early-stop logic in Algorithm 2 is described only in the caption of Figure 2; consider stating it explicitly in the main text of Section 4.5 for clarity.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: CoSAM's reported gains come from held-out target domains; the only self-referential element is reuse of the same group's earlier error-map loss, which is not load-bearing.

full rationale

The central empirical claims are evaluated under a leave-one-domain-out protocol on held-out target domains, with results reported in Tables 1 and 2. The reported DSC values therefore cannot reduce to fitted training values or to target-domain labels reused as predictions. The error decoder is trained on source-domain images with supervision e = XOR(y_hat, y), an externally defined target derived from source labels, and at inference it is applied to unseen target images; the iterative refinement loop uses only the model's own outputs and an early-stop criterion, not target annotations. The self-citations, including reference [44] for the class-balance weight in Eq. (6), point to independently published prior work and are not used to forbid alternatives or to force the benchmark outcomes. There is a genuine internal-consistency concern in Section 4.3: Eq. (4) feeds the perturbed mask y_hat_p to the error decoder, while the error label in Eq. (5) and Algorithm 1 line 9 is computed from the unperturbed binarized mask y_hat, so the decoder is not trained on the exact input it receives at inference. That is a reproducibility and correctness issue, not a circularity: the error map is still supervised by source labels, and the final predictions are not defined in terms of the target outputs they claim to predict.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The hyperparameters alpha, K, T, lambda_r, and lambda_g are tuned by hand (Figure 3 for alpha, K, T); no automated selection is described. The method relies on standard SAM and U-Net components and on the error-map loss from earlier work by the same group [44]. No new physical or algorithmic entities are introduced beyond the trained decoder.

free parameters (6)
  • perturbation probability alpha = 0.2 (prostate), 0.1/0.2 (OD/OC)
    Chosen by hand; Figure 3 shows sensitivity. It controls simulated mask noise in error decoder training.
  • number of point prompts K = 64 (prostate), 8/16 (OD/OC)
    Chosen by hand; Figure 3 shows sensitivity. Balances prompt informativeness versus noise.
  • number of refinement iterations T = 4 (prostate), 1 (OC), 4 (OD)
    Chosen by hand; early stop also applied. Not ablated alone in the paper.
  • refined-loss weight lambda_r = 1.0
    Set to 1.0 without sensitivity analysis.
  • guided-loss weight lambda_g = 0.1 (prostate), 0.1/0.25 (OD/OC)
    Chosen by hand; balances the guided segmentation loss.
  • binarization threshold = 0.5
    Standard threshold for converting soft masks to binary masks; not separately optimized.
assumptions (3)
  • domain assumption SAM's frozen image encoder and mask decoder provide strong representations across domains due to billion-mask pretraining.
    The method relies on SAM's generalization to new distributions, which is a broader assumption about foundation models.
  • domain assumption The error-map loss from [44] is a valid objective for learning error prediction with class imbalance.
    Adopted from prior work without re-derivation.
  • domain assumption Leave-one-domain-out evaluation on two benchmarks is representative of domain generalization performance in medical imaging.
    The evaluation protocol is standard in the DG literature but limited in scope.

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Cite this review

Pith. "Pith review of CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation." pith.science (2026). https://pith.science/paper/EM4N2GYT

@misc{pith2026241110136,
  author       = {Pith},
  title        = {Pith review of: CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EM4N2GYT}},
  note         = {Machine review of arXiv:2411.10136}
}
read the original abstract

Medical images often exhibit distribution shifts due to variations in imaging protocols and scanners across different medical centers. Domain Generalization (DG) methods aim to train models on source domains that can generalize to unseen target domains. Recently, the segment anything model (SAM) has demonstrated strong generalization capabilities due to its prompt-based design, and has gained significant attention in image segmentation tasks. Existing SAM-based approaches attempt to address the need for manual prompts by introducing prompt generators that automatically generate these prompts. However, we argue that auto-generated prompts may not be sufficiently accurate under distribution shifts, potentially leading to incorrect predictions that still require manual verification and correction by clinicians. To address this challenge, we propose a method for 2D medical image segmentation called Self-Correcting SAM (CoSAM). Our approach begins by generating coarse masks using SAM in a prompt-free manner, providing prior prompts for the subsequent stages, and eliminating the need for prompt generators. To automatically refine these coarse masks, we introduce a generalized error decoder that simulates the correction process typically performed by clinicians. Furthermore, we generate diverse prompts as feedback based on the corrected masks, which are used to iteratively refine the predictions within a self-correcting loop, enhancing the generalization performance of our model. Extensive experiments on two medical image segmentation benchmarks across multiple scenarios demonstrate the superiority of CoSAM over state-of-the-art SAM-based methods.

Figures

Figures reproduced from arXiv: 2411.10136 by the authors.

Figure 1
Figure 1. (a) Prompt-free methods directly utilize SAM to produce predictions without prompts. (b) Prompt-based methods train a prompt [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of our proposed CoSAM. (a) The training process of our CoSAM. For the training image and corresponding label, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Overall performance of our CoSAM with various [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.