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REVIEW 4 major objections 6 minor 46 references

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read SAM2-SGP claims that a SAM2-based model can segment medical images without user-supplied prompts by generating pseudo-masks from a support set, and that this prompt-free design outperforms fully supervised models and prior SAM2…

desk verdict Useful SAM2 adaptation idea, but the claimed win over SAM2 baselines rests on a random-click prompt disadvantage and needs a fair rerun before the headline is credible. read the letter →

arxiv 2506.19658 v1 pith:BDCOQTAO submitted 2025-06-24 cs.CV

Yang Xing , Jiong Wu , Yuheng Bu , Kuang Gong This is my paper · ORCID
classification cs.CV
keywords SAM2medicalimagesegmentationsupport-setpromptingpseudo-maskgenerationin-contextlearningLoRA3Dfoundationmodel
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 tries to establish that SAM2, a foundation model trained on natural images and videos, can be made to segment medical images without any human-provided prompts. The proposed framework, SAM2-SGP, feeds a small support set of image-mask pairs into SAM2's memory mechanism to generate a pseudo-mask for each query image, derives a bounding-box prompt from that pseudo-mask, and uses the pseudo-mask to focus attention on relevant regions. A low-rank adaptation layer adapts the frozen encoder to the medical domain. If the claim holds, organ and lesion segmentation no longer requires a radiologist to click points or draw boxes at inference time, and the same model can be applied to fundus, X-ray, ultrasound, CT, MRI, and PET images. Across the reported 2D and 3D benchmarks, the framework reports higher Dice scores than fully supervised baselines such as nnUNet and SwinUNet and than prompt-based SAM2 baselines such as MedSAM2.

What carries the argument

The load-bearing mechanism is the support-set-guided pseudo-mask loop built on SAM2's memory attention. A support-memory encoder adds downsampled support masks to support-image embeddings ($M_s = \phi(y_s) + F_s$); support-memory attention applies self-attention and then cross-attention between query embeddings and these support memories to form query-specific features; a lightweight decoder with an empty prompt produces the pseudo-mask $\tilde{y}_q$. That pseudo-mask then does double duty: it is resized and element-wise multiplied into the attention output of the Pseudo-mask Attention module ($\hat{F}_q = \tilde{y}'_q \odot \mathrm{softmax}(QK^T/\sqrt{d})V + \tilde{F}_q$), and it supplies the bounding-box prompt $B_{\mathrm{box}}$ for the prompt encoder. For 3D data, the same memory machinery is extended with a queue-based volumetric memory bank that appends encoded previous-slice predictions to the support memory.

What would settle it

Run the reported 2D benchmarks again with the K support samples chosen by a random or deliberately poor ranking instead of the paper's similarity criterion. If Dice does not drop substantially, then the claimed support-set-guided prompting mechanism is not what drives the gains; if it does drop, the missing similarity definition becomes the decisive implementation detail.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that SAM2's memory mechanism, built for tracking objects across video frames, can be repurposed as an in-context learner for medical segmentation. The Pseudo-mask Generation module encodes support-set masks together with support-image features, cross-attends these support memories against query-image features, and decodes an initial pseudo-mask with an empty prompt. The Pseudo-mask Attention module turns that pseudo-mask into a bounding-box prompt and multiplies a resized pseudo-mask into the attention output, so the final decoder concentrates on the region the pseudo-mask highlights. For 3D volumes, a third module treats slices as frames, storing previously predicted slices in a memory bank that is concatenated with the support memory, and the memory bank pops the least similar entry as the model propagates along the axial direction. The paper reports that this prompt-free design consistently achieves the highest Dice among all compared methods on REFUGE, PanDental, WBC, CAMUS, BUSI, AMOS22 CT/MRI, PET/CT, and ACDC.

Load-bearing premise

The load-bearing premise is that the K most similar support images can be selected reliably, but the paper never defines the similarity function it uses, so the pseudo-mask, the box prompt, and the final segmentation all inherit whatever errors that unspecified ranking introduces.

Editorial extensions

If this is right

  • Automatic inference: once trained, the model needs only the query image plus the stored support set, so no clicks or boxes are required at test time.
  • Domain adaptation via LoRA: freezing SAM2's image encoder and tuning only low-rank layers is enough to transfer natural-image pretraining to medical modalities, per the reported Dice gains.
  • 3D handled as video: treating axial slices as a temporal sequence makes SAM2's video memory directly applicable to CT, MRI, and PET volumes, with adjacent-slice context stored in a memory bank.
  • Support-set size is a tunable cost-accuracy dial: larger support sets improve reported Dice and IoU up to a point, with size 4 chosen as the default trade-off.
  • Stronger than fully supervised baselines on these benchmarks: the reported Dice scores exceed nnUNet, SwinUNet, and UNETR as well as SAM2 and MedSAM2 on all evaluated 2D and 3D datasets.

Reading between the lines

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

  • Because the K-most-similar selection criterion is never specified, the reported results should be read as conditional on an implicit retrieval quality; a natural extension is to quantify how Dice degrades as the similarity ranking is corrupted or replaced by random selection.
  • The unidirectional slice propagation uses only half of the available 3D context; extending the memory bank to bidirectional propagation is a direct algorithmic next step that could raise accuracy on anisotropic volumes.
  • The framework's prompt-free design suggests a practical deployment pattern: one frozen SAM2 with per-organ or per-modality support sets, with LoRA adapters swapped per domain, rather than retraining a full segmentation network for each task.
  • The pseudo-mask attention's restriction to relevant regions may reduce compute on high-resolution inputs like fundus and panoramic X-ray, though the paper does not report runtime or FLOPs to confirm this.
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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 / 6 minor

Summary. The paper proposes SAM2-SGP, a framework for automatic (prompt-free) medical image segmentation built on SAM2. A support set of image-mask pairs is used by a Pseudo-mask Generation (PMG) module, which adapts SAM2's memory attention to produce a pseudo-mask for each query image. A Pseudo-mask Attention (PMA) module converts the pseudo-mask into a bounding-box prompt and uses the pseudo-mask to gate attention, while LoRA adapts the frozen image encoder to the medical domain. For 3D volumes, a 3D memory module propagates predictions slice-to-slice. The method is evaluated on five 2D datasets and three 3D datasets against UNETR, SwinUNet, nnUNet, SAM2, and MedSAM2, and is reported to achieve the highest Dice in most settings. Ablations examine the contributions of the PMG, PMA, and 3DM modules, support-set size, and SAM2 pretrained weights. The central claim is that SAM2-SGP consistently outperforms both fully supervised models and SAM2-based approaches across modalities without manual prompting.

Significance. If the results hold under a fair comparison protocol, the paper makes a useful empirical contribution: it shows a concrete way to remove manual prompting from SAM2-style medical segmentation while retaining the benefits of foundation-model priors, through in-context support sets and pseudo-mask-guided attention. The paper also ships code and includes ablations for each proposed module, which are commendable for reproducibility. The architectural description is backed by equations and figures, and the use of LoRA to adapt a frozen encoder is sensible. However, the significance is currently tempered by a comparison protocol that appears unfair to the promptable baselines, by an undefined support-set similarity measure that undermines exact reproducibility, and by the absence of any error bars or significance tests. These issues affect the paper's central claim of consistent superiority and must be addressed before the contribution can be fully assessed.

major comments (4)
  1. [Section 5.1, Tables 1-4] The evaluation of SAM2 and MedSAM2 uses a random click as the prompt, as stated in Section 5.1, while SAM2-SGP automatically derives a bounding-box prompt from its pseudo-mask. Because SAM2 and MedSAM2 are prompt-dependent segmenters, a random click that often lands outside or on the boundary of the target structure cannot be considered a comparable baseline. The large reported gaps (e.g., REFUGE 0.753 vs. 0.865 and WBC 0.627 vs. 0.976) may therefore reflect the prompt information advantage rather than the contribution of the proposed modules. To substantiate the central claim, the authors should compare against SAM2/MedSAM2 using equivalent automatic prompts (e.g., ground-truth or predicted box prompts, or a multi-click protocol) and report both the prompt protocol and the resulting scores.
  2. [Section 3.1, Eq. (5)-(6)] The support-set selection is not reproducible: the paper states that 'we selected a subset of the support set consisting of the K most similar samples' but never defines the similarity function. Since the PMG module's pseudo-mask quality depends directly on this ranking, and for 3D data the memory bank also stores the model's own previous predictions (Eqs. 5-6), an undefined selection procedure makes the method impossible to re-implement exactly and leaves the failure mode of early poor selections unexamined. The authors should specify the similarity metric (e.g., feature distance, image-level Dice of pseudo-masks, or DINOv2 features) and describe how the selection is performed during training and inference.
  3. [Section 4.1.2 and Section 5.2] The dataset descriptions do not match the cited references. The head-and-neck PET/CT dataset is introduced with reference [36] as '100 3D PET images with a resolution of 124x124 pixels per slice,' but Section 5.2 evaluates on 'the PET/CT dataset [42],' which is described in the bibliography as a PET image denoising dataset. Similarly, the 'Panoramic dental dataset' is cited to reference [32], which is titled 'Automatic segmentation of mandible in panoramic x-ray' and does not describe an over-1,000-image tooth-and-bone segmentation dataset as stated. These mismatches make the exact evaluation conditions ambiguous and should be corrected with the proper dataset references and scanning protocols.
  4. [Tables 1-5] No error bars, confidence intervals, or statistical significance tests are reported for any of the Dice comparisons. Several reported differences are small (e.g., CAMUS 0.932 vs. 0.925; ACDC average 0.917 vs. 0.916; Table 5 right kidney 0.952 vs. 0.953 across ablations), so without variance estimates the 'consistent' superiority claim is not established even under a fair prompt protocol. The authors should report results over multiple random seeds or cross-validation folds and, where appropriate, a paired significance test.
minor comments (6)
  1. [Throughout] There are several typos and inconsistencies: 'Pesudo Mask Decoder' in Figure 2, 'Pandental' in Figure 4, 'outputed' in Section 3.2, and inconsistent capitalization of 'medSAM2' versus 'MedSAM2' between text and tables.
  2. [Abstract and Section 1] The word 'significant' is used to describe performance improvements, but no statistical significance is established; please replace with 'substantial' or add significance tests.
  3. [Section 4.2] The hyperparameter details ('learning rates and batch sizes were tuned') are too vague for reproduction; please list the actual values per dataset, including LoRA rank and alpha, loss weights in Eq. (9), and the memory bank size.
  4. [Section 5.3.2, Table 6] The support-set-size ablation is described as duplicating a subset of training samples so that 'the training distribution remained unchanged'; please clarify whether the support set is disjoint from the query set used for training, and whether any samples in the support set also appear among the training queries, to rule out trivial memorization.
  5. [Table 3] The organ-wise results show that SAM2-SGP is not best on all organs (e.g., pancreas 0.851 vs. nnUnet 0.874; esophagus 0.852 vs. nnUnet 0.857), which is fine, but the text should acknowledge this nuance when claiming consistent superiority.
  6. [Section 5.2, Table 4] The ACDC results show lower Dice for left ventricle and myocardium than some baselines; the text mentions this but should also explain whether this is due to the unidirectional 3D propagation limitation mentioned in Section 6.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: SAM2-SGP is a forward architecture trained with ground-truth Dice/CE losses; the KL term is a consistency regularizer, and self-cited datasets are evaluation inputs, not load-bearing premises.

full rationale

SAM2-SGP is an empirical systems paper rather than a formal derivation, and its central claim rests on benchmark comparisons, not on a chain of equations that reduces to its own inputs. The PMG module (Eqs. 2-4) maps support-set image-mask pairs and query embeddings to an intermediate pseudo-mask; the PMA module (Eqs. 7-8) uses that pseudo-mask only to construct a bounding-box prompt and to gate attention; the final loss (Eq. 9) is a weighted sum of Dice, cross-entropy, and KL terms. The KL term couples the pseudo-mask and the final prediction, but both are anchored to the ground truth y_q by the Dice and CE terms, so this is a coherence regularizer rather than a case where the predicted quantity is defined as the input. No parameter is fitted to test-set labels, and the support set is drawn from the training split, which is standard in-context learning rather than circular evidence. The self-citations ([36], [42]) supply the PET/CT evaluation dataset; they do not define any equation in the method and the main comparison does not reduce to them. The random-click protocol for SAM2/MedSAM2 baselines and the unspecified similarity measure for support-set selection are legitimate experimental-fairness and reproducibility concerns, but they are not circular reductions of the paper's derivation. The bulk of evaluation is on public datasets, and the proposed modules are described with explicit forward equations. I therefore find no load-bearing circular step.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper's central empirical claim rests on several unstated hyperparameters and domain assumptions. The components are well-known building blocks, and no new physical entities are introduced. The most significant ledger entries are the unspecified support-set similarity metric and the selection of support-set size based on a non-monotonic ablation.

free parameters (6)
  • Loss weights lambda_dice, lambda_ce, lambda_KL = Not reported
    Eq. 9 defines the training loss with three weighting hyperparameters; no default values are given, so the reader cannot reproduce the training balances.
  • Support-set size K = 4 (default)
    Section 5.3.2 ablates support sizes 1, 2, 4, 8, 16 and selects 4 as 'optimal balance'; the ablation shows non-monotonic Dice/IoU (e.g., BUSI 0.7699 at size 2 vs 0.7673 at size 4), so the choice is post hoc.
  • LoRA rank and alpha = Not reported
    Section 3.3 says LoRA layers are integrated but does not give rank or scaling hyperparameters.
  • Learning rate and batch size = Tuned per dataset, not reported
    Section 4.2 says hyperparameters 'were tuned for optimal performance across different datasets' but no values are listed.
  • Memory bank size for 3D = Equal to support-set size by default
    Section 3.5 says the memory bank size was set equal to the support-set size by default; this couples 3D context capacity to the support-set size without ablation.
  • Support-set similarity metric = Unspecified
    Section 3.1 selects 'the K most similar samples' but does not define the similarity function; the quality of pseudo-masks and thus the final segmentation depends on this unstated metric.
assumptions (4)
  • domain assumption SAM2's memory mechanism transfers to cross-image in-context learning.
    Section 3.4 assumes the memory encoder/attention designed for video tracking provides useful cross-image correlation for support-set-guided pseudo-mask generation; no dedicated study validates this beyond the main results.
  • domain assumption 3D medical volumes can be treated as temporal video sequences along one axis.
    Section 3.5 propagates predictions slice by slice along the axial direction; the authors themselves note in Section 6 that this unidirectional propagation uses only half of the adjacent context.
  • ad hoc to paper The K most similar support samples can be chosen reliably with an undefined similarity measure.
    Section 3.1 selects a subset of the K most similar samples but never specifies the similarity function; the method's auto-prompting quality hinges on this selection.
  • domain assumption Pretrained SAM2 weights benefit medical segmentation despite domain shift.
    Section 5.3.3 ablates with and without pretrained weights and shows large gains, but this is an empirical assumption about transferability across a major domain shift, not derived.

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Pith. "Pith review of SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting." pith.science (2026). https://pith.science/paper/BDCOQTAO

@misc{pith2026250619658,
  author       = {Pith},
  title        = {Pith review of: SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BDCOQTAO}},
  note         = {Machine review of arXiv:2506.19658}
}
read the original abstract

Although new vision foundation models such as Segment Anything Model 2 (SAM2) have significantly enhanced zero-shot image segmentation capabilities, reliance on human-provided prompts poses significant challenges in adapting SAM2 to medical image segmentation tasks. Moreover, SAM2's performance in medical image segmentation was limited by the domain shift issue, since it was originally trained on natural images and videos. To address these challenges, we proposed SAM2 with support-set guided prompting (SAM2-SGP), a framework that eliminated the need for manual prompts. The proposed model leveraged the memory mechanism of SAM2 to generate pseudo-masks using image-mask pairs from a support set via a Pseudo-mask Generation (PMG) module. We further introduced a novel Pseudo-mask Attention (PMA) module, which used these pseudo-masks to automatically generate bounding boxes and enhance localized feature extraction by guiding attention to relevant areas. Furthermore, a low-rank adaptation (LoRA) strategy was adopted to mitigate the domain shift issue. The proposed framework was evaluated on both 2D and 3D datasets across multiple medical imaging modalities, including fundus photography, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound. The results demonstrated a significant performance improvement over state-of-the-art models, such as nnUNet and SwinUNet, as well as foundation models, such as SAM2 and MedSAM2, underscoring the effectiveness of the proposed approach. Our code is publicly available at https://github.com/astlian9/SAM_Support.

Figures

Figures reproduced from arXiv: 2506.19658 by the authors.

Figure 1
Figure 1. A comparison between (a) SAM2 and (b) the proposed SAM2-SGP. More details of SAM2-SGP are shown in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Diagram of the proposed framework. It contained four modules: (a) feature extraction (FE) module, (b) pseudo-mask generation (PMG) module, (c) pseudo-mask attention (PMA) module, and (d) 3D memory encoding (3DM) module. The 3D memory encoding module was only enabled when processing 3D datasets. verseg’s design and combined the ICL model and the SAM model for segmentation tasks. We observed that ICL condi￾tioned quer… view at source ↗
Figure 3
Figure 3. Examples of segmentation results from the CAMUS, BUSI, and REFUGE datasets. For each sub-figure, the first column shows the image, and subsequent columns present results from ground truth and 6 comparison methods. The two rows represent two different cases. (a) Segmentation results of the CAMUS dataset. The left ventricle, the atrium, and the myocardium were labelled in red, yellow, and cyan, respectively. (b) Segme… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Examples of segmentation results from the Pandental and WBC datasets. For each sub-figure, the first column shows the image, and subsequent columns present results from ground truth and 6 comparison methods. The two rows represent two different cases. (a) Segmentation …
Figure 5
Figure 5. Figure 5: Examples of segmentation results from the PET/CT 3D dataset. The two rows represent the sagittal and axial views. The first column shows the PET image, the second column shows the CT image, and subsequent columns present segmentation results (labelled in pink) from gro…
Figure 6
Figure 6. Figure 6: Examples of segmentation results from the AMOS22 3D dataset. Different organs were labeled in different colors, including left kidney (in green), spleen (in cyan), postcava (in orange), and pancreas (in red). The first column shows the image, and subsequent columns pre…
Figure 7
Figure 7. Figure 7: Examples of segmentation results from the ACDC 3D dataset. Left ventricle, right ventricle, and myocardium were labelled in red, yellow, and cyan, respectively. The first column shows the image, and subsequent columns present results from ground truth and 6 comparison …

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Pith tools

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