Pith. sign in

REVIEW 4 major objections 4 minor 38 references

SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation

T0 review · 4 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A Mamba-Prior module injected into a frozen SAM encoder improves polyp segmentation on seen and unseen colonoscopy datasets, beating the CNN and ViT baselines it is compared with.

desk verdict A novel SAM+Mamba adapter architecture worth a serious look, but the SOTA claim is built on borrowed baseline numbers that were never re-run under the same protocol. read the letter →

arxiv 2412.08482 v1 pith:P3P2N4ZA submitted 2024-12-11 cs.CV

Tapas Kumar Dutta , Snehashis Majhi , Deepak Ranjan Nayak , Debesh Jha This is my paper · ORCID
classification cs.CV
keywords polypsegmentationzero-shotgeneralizationSegmentAnythingModelMambastatespacemodelsadapterfine-tuningcolonoscopymulti-scalefeaturedecomposition
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

Polyp segmentation in colonoscopy has to cope with polyps that vary in size, color, and boundary clarity, and a clinically useful model should also transfer to images from devices it has never seen. This paper proposes SAM-Mamba, which keeps the Segment Anything Model's image encoder frozen and adds a Mamba-Prior module so the general-purpose SAM features are steered toward polyp-specific cues. On a five-dataset protocol in which 1,450 images from Kvasir-SEG and CVC-ClinicDB form the training set and three other datasets are held out, the paper reports that this design beats the CNN and ViT baselines it is compared with, with the largest gaps on the held-out datasets—margins the paper puts at about +4 points on CVC-ColonDB and +3.8 points on ETIS. If the results hold, the approach is a lightweight way to repurpose a large segmentation foundation model for a medical domain without full fine-tuning.

What carries the argument

The load-bearing component is the Mamba-Prior module, whose named pieces are Multi-scale Spatial Decomposition (MSD), Channel Saliency and Context Accumulation, and Mamba Channel Interaction. MSD builds a spatial feature pyramid with parallel 3x3, 5x5, and 7x7 convolutions; global max and average pooling then separate each scale into salient and contextual cues; and two Mamba layers—state-space models that process sequences with linear complexity—encode long-range dependencies over those channel distributions. The gated Mamba outputs are multiplied back into the multi-scale feature map and concatenated to form the prior, which enters SAM's frozen ViT blocks through cross-attention adapters. A pseudo-mask produced from the adapted encoder is later fed to SAM's decoder as the prompt, removing the need for user-supplied points or boxes, and the network is trained in two stages: encoder-side adapters first under deep supervision, then the decoder with the full mask loss.

What would settle it

Re-running CTNet, MEGANet, Polyp-PVT, and the other compared methods under SAM-Mamba's exact protocol (1,450 training images, 352x352 inputs, same augmentation, same evaluation protocol) and finding that any baseline reaches or exceeds 78.2% mIoU on ETIS or 77.1% mIoU on CVC-ColonDB would show the claimed zero-shot margins are not reproducible.

Watch

Extended reading notes

Core claim

The central claim is that a Mamba-based prior, rather than unfreezing SAM or adding many parameters, is what unlocks strong polyp segmentation with zero-shot generalization. The Mamba-Prior module decomposes the input image at three spatial scales using 3x3, 5x5, and 7x7 convolutions, pools the scales into separate saliency and context channels, and runs both through Mamba layers that model long-range channel dependencies at linear cost; the gated outputs are then multiplied back into the original feature map to form a domain prior that is injected into SAM's frozen ViT blocks through cross-attention adapters. On the seen datasets the model reaches 92.4% mean Dice on Kvasir-SEG and 94.2% on CVC-ClinicDB, while on the unseen CVC-ColonDB and ETIS datasets it reports 77.1% and 78.2% mean intersection-over-union, ahead of every compared CNN and ViT baseline. Ablations attribute the gain to the combination of multi-scale decomposition and Mamba, with single-scale variants losing several points on unseen datasets.

Load-bearing premise

The claimed superiority rests on baseline numbers quoted from other papers, with the paper itself noting in Section 4.3 that PEFNet's numbers come from M2UNet, rather than on re-runs under SAM-Mamba's exact protocol; if those quoted numbers were obtained under different preprocessing, training, or evaluation details, the margins could shrink or disappear.

Editorial extensions

If this is right

  • If the reported numbers hold, a frozen SAM encoder plus a Mamba-Prior module beats specialized CNN and ViT polyp segmenters on both seen and unseen benchmark datasets.
  • The pseudo-mask prompting pipeline makes the model usable as a fully automatic colonoscopy screening tool, since no point, box, or text prompt is required at inference.
  • The module adds only about 9.5% more parameters (from 94M to 103M), so the accuracy gain does not depend on full fine-tuning of SAM.
  • According to the ablations, multi-scale decomposition is necessary for generalization: single-kernel variants lose roughly 3–4 points on the unseen datasets, while the full multi-scale-plus-Mamba configuration is best everywhere.

Reading between the lines

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

  • A direct test the paper leaves open is whether the Mamba-Prior transfers to other SAM-based medical segmentation tasks, such as organ or lesion segmentation; the same style of gain would be expected if the prior is the active ingredient.
  • Because all baselines are quoted from prior publications rather than re-run, re-benchmarking them in one codebase with identical preprocessing and evaluation would either confirm or erode the reported margins.
  • The module's combination of multi-scale spatial decomposition and saliency/context separation is not tied to SAM in principle; applying it to other foundation-segmentation backbones could reveal whether the benefit comes from the prior or from SAM's frozen representation.
  • The abstract claims superiority over Adapter-based models, but no adapter-only baseline appears in the reported tables; a head-to-head comparison with existing SAM-adapter methods would make that claim testable.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes SAM-Mamba, an adapter-based fine-tuning of the Segment Anything Model (SAM) for polyp segmentation. A Mamba-Prior module, composed of multi-scale spatial decomposition (MSD), channel saliency/context pooling, and Mamba channel interaction, injects domain priors into the frozen SAM encoder. The method is trained on Kvasir-SEG and CVC-ClinicDB and evaluated in a zero-shot generalization setting on CVC-300, CVC-ColonDB, and ETIS. The authors report that SAM-Mamba outperforms CNN-, ViT-, and adapter-based models, and they provide quantitative comparisons against 14 baselines plus ablation studies.

Significance. If the claimed results are protocol-equivalent, SAM-Mamba would represent a strong new state of the art for generalized zero-shot polyp segmentation, with notable margins on unseen datasets. The architectural idea of combining SSM-based Mamba layers with SAM adapters is motivated, and the ablation study provides some evidence that the MSD and Mamba components each help. The release of code and models via GitHub is a strength that supports reproducibility. However, the significance is currently conditional, because the headline comparison relies on borrowed baseline numbers and contains an internal contradiction.

major comments (4)
  1. [§4.3, Table 1] The text states that on CVC-ClinicDB SAM-Mamba "outperforms peers in key metrics such as mIoU, Fβ, and Sα," but Table 1 shows MEGANet at 89.4 mIoU versus 88.7 for SAM-Mamba. The mIoU claim is therefore contradicted by the paper's own table. Please correct either the table or the text, and ensure that all reported "outperforms" statements are consistent with the numbers shown.
  2. [Abstract, §4.3, Tables 1-3] The abstract and Section 4.3 claim superiority over "Adapter-based models," but no adapter-based baseline appears in Tables 1-3; the only adapter comparison is the internal ablation in Table 4. Without at least one external adapter-based baseline (e.g., Medical SAM Adapter or a comparable SAM fine-tuning method) evaluated under the same protocol, the claim as stated is not supported by the presented evidence.
  3. [§4.2, §4.3] All baseline results are taken from the original papers, and PEFNet's numbers are taken from M2UNet, while SAM-Mamba is trained with 352x352 input, multi-scale augmentation, and 200 epochs. Since no baseline is re-run under SAM-Mamba's protocol, protocol differences in preprocessing, training split, and evaluation could account for part of the reported gains (e.g., +4.0 and +3.8 mIoU on CVC-ColonDB and ETIS). The zero-shot superiority claim needs either a like-for-like comparison for the most competitive baselines or an explicit justification that the borrowed numbers are protocol-equivalent.
  4. [§4.3, Tables 2-3] The claim of "outperforming all SOTA models" on unseen datasets is stated as a margin over the next best method, but on several metrics the advantage is small or reversed (e.g., SAM-Mamba's Fβ on CVC-300 is 88.8 versus CTNet's 89.4, and its E^max on CVC-ClinicDB is below several baselines). Please report the comparison more precisely, and avoid selective metric reporting when summarizing results.
minor comments (4)
  1. [§3.2.3, Figure 1] The notation f(θ) is used to describe the pseudo-mask extraction, but f(θ) is never formally defined in the text; please specify what this function and its parameter space θ represent.
  2. [Equation (3)] In Equation (3), the second concatenation operand is written as "MC_o ⊙ M*", which appears to be a typo for "M^C_o ⊙ M*"; please ensure superscript notation is consistent throughout.
  3. [Table 2] In the CVC-300 block, the CFA-Net row lists Fβ=93.8 and Sα=87.5; since Fβ is normally bounded by structural similarity, please verify these entries or explain why their relative ordering is plausible.
  4. [Abstract, Title] The dagger symbol after "SAM-Mamba" in the abstract and the footnote marker do not appear to be resolved anywhere in the text; please either add the promised footnote or remove the symbol.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical architecture study whose results come from external benchmark evaluation, not from a derivation that reduces to its inputs.

full rationale

SAM-Mamba is an empirical architecture paper. Its central claim, outperforming prior CNN, ViT, and adapter-based methods on polyp segmentation, is supported by evaluations on five public datasets under the standard PraNet-style split, with the model trained on the seen split and tested on unseen datasets. There is no fitted parameter, no theory-derived constant, and no equation in Sections 3.2.1–3.2.4 whose output is defined in terms of the claimed result; the Mamba-Prior module and adapters are trained with Dice/BCE losses against ground truth and then evaluated externally. The paper's use of the authors' Kvasir-SEG dataset is citation of a public benchmark, not load-bearing self-citation. The note in Section 4.3 that PEFNet results are sourced from M2UNet and that other baselines are taken from their original works is a transparency statement about borrowed metrics; it raises a legitimate protocol-comparability concern, but it is not circularity because SAM-Mamba's own numbers are independently computed and the quoted baseline numbers do not by construction determine the reported margins. No step in the paper reduces a prediction to a fit or renames an input as an output, so the appropriate finding is no significant circularity.

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

The central claim is empirical, so the ledger records the hand-chosen hyperparameters and the unproved assumptions about transferability and benchmark comparability that the claimed performance rests on. No new theoretical objects are introduced.

free parameters (6)
  • Learning rate = 1e-5
    Chosen without sensitivity analysis; affects convergence and final performance.
  • Training epochs = 200
    Chosen without reporting validation curves or early stopping; affects final metrics.
  • Input resolution = 352x352
    Standard choice in polyp segmentation, not ablated here.
  • MSD kernel sizes = {3,5,7}
    Ablation compares multi-scale vs uni-scale but not other kernel sets.
  • Loss weights (w in Dice/BCE) = not specified
    The paper uses weighted Dice and BCE but never states the weights.
  • MSD output channels C0 = not specified
    Channel count in the multi-scale pyramid is not reported.
assumptions (4)
  • domain assumption SAM's frozen pretrained features are transferable to polyp images through adapter-based fine-tuning
    The whole method rests on this; the paper cites prior adapter works but does not prove it for this setting.
  • domain assumption The PraNet benchmark split and the six evaluation metrics are valid proxies for clinical polyp segmentation quality
    Used without justification; no clinical validation is presented.
  • ad hoc to paper Global max/avg pooling followed by Mamba channel interaction captures useful multi-scale context for polyps
    This is the core architectural bet; the ablation shows it helps empirically on the tested datasets, but there is no analysis of why it should generalize.
  • domain assumption Baseline results reported in prior papers are directly comparable to the authors' runs
    Required for all SOTA comparisons; not verified by re-running baselines.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation." pith.science (2026). https://pith.science/paper/P3P2N4ZA

@misc{pith2026241208482,
  author       = {Pith},
  title        = {Pith review of: SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P3P2N4ZA}},
  note         = {Machine review of arXiv:2412.08482}
}
read the original abstract

Polyp segmentation in colonoscopy is crucial for detecting colorectal cancer. However, it is challenging due to variations in the structure, color, and size of polyps, as well as the lack of clear boundaries with surrounding tissues. Traditional segmentation models based on Convolutional Neural Networks (CNNs) struggle to capture detailed patterns and global context, limiting their performance. Vision Transformer (ViT)-based models address some of these issues but have difficulties in capturing local context and lack strong zero-shot generalization. To this end, we propose the Mamba-guided Segment Anything Model (SAM-Mamba) for efficient polyp segmentation. Our approach introduces a Mamba-Prior module in the encoder to bridge the gap between the general pre-trained representation of SAM and polyp-relevant trivial clues. It injects salient cues of polyp images into the SAM image encoder as a domain prior while capturing global dependencies at various scales, leading to more accurate segmentation results. Extensive experiments on five benchmark datasets show that SAM-Mamba outperforms traditional CNN, ViT, and Adapter-based models in both quantitative and qualitative measures. Additionally, SAM-Mamba demonstrates excellent adaptability to unseen datasets, making it highly suitable for real-time clinical use.

Figures

Figures reproduced from arXiv: 2412.08482 by the authors.

Figure 1
Figure 1. Overview of the SAM-Mamba framework for polyp segmentation. The architecture constitutes the SAM backbone with the Mamba-Prior module and Adapter-based fine-tuning to enhance adaptability for polyp segmentation, addressing challenges like zero-shot feature transfer-ability, computational cost, and prompt dependency in SAM. in polyps. Recently, a Multi-scale Edge-Guided Attention Network called MEGANet was designed i… view at source ↗
Figure 2
Figure 2. Qualitative comparison on seen datasets (Kvasir-SEG and CVC-ClinicDB), showcasing the model’s ability to accurately segment [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison on unseen datasets (CVC-300, CVC-ColonDB, and ETIS), highlighting the model’s superior generaliza [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Illustration of the sequence learning progression within the SAM-Mamba model through a set of heatmap visualizations: the [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 27 canonical work pages

  1. [1]

    Medical image segmentation review: The suc- cess of u-net

    Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland, Yiwei Jia, Atlas Haddadi Avval, Afshin Bozorgpour, Sanaz Karimijafarbigloo, Joseph Paul Cohen, Ehsan Adeli, and Dorit Merhof. Medical image segmentation review: The suc- cess of u-net. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. 1

    work page 2024

  2. [2]

    Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs

    Jorge Bernal, F Javier S ´anchez, Gloria Fern ´andez- Esparrach, Debora Gil, Cristina Rodr ´ıguez, and Fernando Vilari˜no. Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics , 43:99–111,

    work page

  3. [3]

    Meganet: Multi-scale edge- guided attention network for weak boundary polyp segmen- tation

    Nhat-Tan Bui, Dinh-Hieu Hoang, Quang-Thuc Nguyen, Minh-Triet Tran, and Ngan Le. Meganet: Multi-scale edge- guided attention network for weak boundary polyp segmen- tation. In Proceedings of the IEEE/CVF Winter Confer- ence on Applications of Computer Vision, pages 7985–7994,

    work page

  4. [4]

    Transunet: Transformers make strong encoders for medi- cal image segmentation

    Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L Yuille, and Yuyin Zhou. Transunet: Transformers make strong encoders for medi- cal image segmentation. arXiv preprint arXiv:2102.04306,

    arXiv

  5. [5]

    Vision transformer adapter for dense predictions

    Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, and Yu Qiao. Vision transformer adapter for dense predictions. arXiv preprint arXiv:2205.08534, 2022. 2, 5

    arXiv 2022

  6. [6]

    Polyp-pvt: Polyp segmen- tation with pyramid vision transformers

    Bo Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huazhu Fu, and Ling Shao. Polyp-pvt: Polyp segmen- tation with pyramid vision transformers. arXiv preprint arXiv:2108.06932, 2021. 6, 7

    arXiv 2021

  7. [7]

    An image is worth 16x16 words: Trans- formers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, et al. An image is worth 16x16 words: Trans- formers for image recognition at scale. International Con- ference on Learning Representations, 2021. 1

    work page 2021

  8. [8]

    Structure-measure: A new way to evaluate foreground maps

    Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji. Structure-measure: A new way to evaluate foreground maps. In Proceedings of IEEE International Conference on Computer Vision, pages 4548–4557, 2017. 5

    work page 2017

Show all 38 references
  1. [9]

    Enhanced-alignment mea- sure for binary foreground map evaluation

    Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming- Ming Cheng, and Ali Borji. Enhanced-alignment mea- sure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421, 2018. 5

    2018 arXiv

  2. [10]

    Pranet: Parallel reverse attention network for polyp segmentation

    Deng-Ping Fan, Ge-Peng Ji, Tao Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, and Ling Shao. Pranet: Parallel reverse attention network for polyp segmentation. In Medical Image Computing and Computer Assisted Intervention (MICCAI) , pages 263–273, 2020. 1, 2, 5, 6, 7

    2020

  3. [11]

    Selective feature aggregation network with area-boundary constraints for polyp segmentation

    Yuqi Fang, Cheng Chen, Yixuan Yuan, and Kai-yu Tong. Selective feature aggregation network with area-boundary constraints for polyp segmentation. In 22nd International Conference on Medical Image Computing and Computer As- sisted Intervention (MICCAI 2019), pages 302–310, 2019...

    2019

  4. [12]

    Utnet: a hybrid transformer architecture for medical image segmen- tation

    Yunhe Gao, Mu Zhou, and Dimitris N Metaxas. Utnet: a hybrid transformer architecture for medical image segmen- tation. In 24th International Conference on Medical Image Computing and Computer Assisted Intervention–MICCAI 2021, pages 61–71, 2021. 1

    2021

  5. [13]

    Unetr: Transformers for 3d medical image segmentation

    Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R Roth, and Daguang Xu. Unetr: Transformers for 3d medical image segmentation. In Proceedings of the IEEE/CVF Win- ter Conference on Applications of Computer Vision , pages 574–5...

    2022

  6. [14]

    Kvasir-seg: A segmented polyp dataset

    Debesh Jha, Pia H Smedsrud, Michael A Riegler, P ˚al Halvorsen, Thomas De Lange, Dag Johansen, and H˚avard D Johansen. Kvasir-seg: A segmented polyp dataset. In MultiMedia modeling: 26th international conference, MMM 2020, Daejeon, South Korea, January 5–8, 2020, proceed- ings...

    2020

  7. [15]

    Resunet++: An advanced architecture for medical image segmentation

    Debesh Jha, Pia H Smedsrud, Michael A Riegler, Dag Jo- hansen, Thomas De Lange, P ˚al Halvorsen, and H ˚avard D Johansen. Resunet++: An advanced architecture for medical image segmentation. In 2019 IEEE International Symposium on Multimedia (ISM), pages 225–2255, 2019. 1, 2

    2019

  8. [16]

    Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C. Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick. Segment anything. arXiv:2304.02643, 2023. 2, 5

    2023 arXiv

  9. [17]

    Ds-transunet: Dual swin transformer u-net for medical image segmentation

    Ailiang Lin, Bingzhi Chen, Jiayu Xu, Zheng Zhang, Guang- ming Lu, and David Zhang. Ds-transunet: Dual swin transformer u-net for medical image segmentation. IEEE Transactions on Instrumentation and Measurement , 71:1– 15, 2022. 1

    2022

  10. [18]

    U-mamba: Enhancing long-range dependency for biomedical image segmentation

    Jun Ma, Feifei Li, and Bo Wang. U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722, 2024. 2

    2024 arXiv

  11. [19]

    How to evaluate foreground maps? In Proceedings of IEEE Con- ference on Computer Vision and Pattern Recognition, pages 248–255, 2014

    Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal. How to evaluate foreground maps? In Proceedings of IEEE Con- ference on Computer Vision and Pattern Recognition, pages 248–255, 2014. 5

    2014

  12. [20]

    Pefnet: Positional embedding feature for polyp segmentation

    Trong-Hieu Nguyen-Mau, Quoc-Huy Trinh, Nhat-Tan Bui, Phuoc-Thao V o Thi, Minh-Van Nguyen, Xuan-Nam Cao, Minh-Triet Tran, and Hai-Dang Nguyen. Pefnet: Positional embedding feature for polyp segmentation. In International Conference on Multimedia Modeling, pages 240–251, 2023. 2, 6, 7

    2023

  13. [21]

    Medical im- age segmentation via cascaded attention decoding

    Md Mostafijur Rahman and Radu Marculescu. Medical im- age segmentation via cascaded attention decoding. In Pro- ceedings of the IEEE/CVF Winter Conference on Applica- tions of Computer Vision, pages 6222–6231, 2023. 2, 3, 6, 7

    2023

  14. [22]

    U-net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In 18th international conference on Medical image com- puting and computer-assisted intervention (MICCAI 2015) , pages 234–241, 2015. 1, 6, 7

    2015

  15. [23]

    Medical image segmentation: A review of modern architec- tures

    Natalia Salpea, Paraskevi Tzouveli, and Dimitrios Kollias. Medical image segmentation: A review of modern architec- tures. In European Conference on Computer Vision , pages 691–708, 2022. 1

    2022

  16. [24]

    Transformers in medical imaging: A survey

    Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, and Huazhu Fu. Transformers in medical imaging: A survey. Medical Image Analysis, 88:102802, 2023. 1

    2023

  17. [25]

    Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer

    Juan Silva, Aymeric Histace, Olivier Romain, Xavier Dray, and Bertrand Granado. Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer. International Journal of Computer Assisted Radiology and Surgery, 9:283–293, 2014. 5

    2014

  18. [26]

    Automated polyp detection in colonoscopy videos using shape and context information

    Nima Tajbakhsh, Suryakanth R Gurudu, and Jianming Liang. Automated polyp detection in colonoscopy videos using shape and context information. IEEE Transactions on Medical Imaging, 35(2):630–644, 2015. 5

    2015

  19. [27]

    M2unet: Metaformer multi-scale up- sampling network for polyp segmentation

    Quoc-Huy Trinh, Nhat-Tan Bui, Trong-Hieu Nguyen-Mau, Minh-Van Nguyen, Hai-Minh Phan, Minh-Triet Tran, and Hai-Dang Nguyen. M2unet: Metaformer multi-scale up- sampling network for polyp segmentation. In 2023 31st European Signal Processing Conference (EUSIPCO), pages 1115–1119,...

    2023

  20. [28]

    A benchmark for en- doluminal scene segmentation of colonoscopy images

    David V ´azquez, Jorge Bernal, F Javier S ´anchez, Gloria Fern´andez-Esparrach, Antonio M L ´opez, Adriana Romero, Michal Drozdzal, and Aaron Courville. A benchmark for en- doluminal scene segmentation of colonoscopy images. Jour- nal of Healthcare Engineering, 2017(1):4037190...

    2017

  21. [29]

    Shallow attention network for polyp seg- mentation

    Jun Wei, Yiwen Hu, Ruimao Zhang, Zhen Li, S Kevin Zhou, and Shuguang Cui. Shallow attention network for polyp seg- mentation. In 24th International Conference on Medical Im- age Computing and Computer Assisted Intervention (MIC- CAI 2021), pages 699–708, 2021. 2, 6, 7

    2021

  22. [30]

    Medical sam adapter: Adapting seg- ment anything model for medical image segmentation.arXiv preprint arXiv:2304.12620, 2023

    Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu, Min Xu, Yanwu Xu, and Yueming Jin. Medical sam adapter: Adapting seg- ment anything model for medical image segmentation.arXiv preprint arXiv:2304.12620, 2023. 2

    2023 arXiv

  23. [31]

    Ctnet: Contrastive transformer network for polyp seg- mentation

    Bin Xiao, Jinwu Hu, Weisheng Li, Chi-Man Pun, and Xiuli Bi. Ctnet: Contrastive transformer network for polyp seg- mentation. IEEE Transactions on Cybernetics, 2024. 2, 3, 6, 7

    2024

  24. [32]

    Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation

    Zhaohu Xing, Tian Ye, Yijun Yang, Guang Liu, and Lei Zhu. Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation. arXiv preprint arXiv:2401.13560, 2024. 2

    2024 arXiv

  25. [33]

    M2snet: Multi-scale in multi-scale subtraction network for medi- cal image segmentation

    Xiaoqi Zhao, Hongpeng Jia, Youwei Pang, Long Lv, Feng Tian, Lihe Zhang, Weibing Sun, and Huchuan Lu. M2snet: Multi-scale in multi-scale subtraction network for medi- cal image segmentation. arXiv preprint arXiv:2303.10894,

    arXiv

  26. [34]

    Automatic polyp segmentation via multi-scale subtraction network

    Xiaoqi Zhao, Lihe Zhang, and Huchuan Lu. Automatic polyp segmentation via multi-scale subtraction network. In 24th International Conference on Medical Image Computing and Computer Assisted Intervention–MICCAI 2021 , pages 120–130. Springer, 2021. 1, 2, 5, 6, 7

    2021

  27. [35]

    Can sam segment polyps? arXiv preprint arXiv:2304.07583,

    Tao Zhou, Yizhe Zhang, Yi Zhou, Ye Wu, and Chen Gong. Can sam segment polyps? arXiv preprint arXiv:2304.07583,

    arXiv

  28. [36]

    Cross-level feature aggre- gation network for polyp segmentation.Pattern Recognition, 140:109555, 2023

    Tao Zhou, Yi Zhou, Kelei He, Chen Gong, Jian Yang, Huazhu Fu, and Dinggang Shen. Cross-level feature aggre- gation network for polyp segmentation.Pattern Recognition, 140:109555, 2023. 1, 2, 6, 7

    2023

  29. [37]

    Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation

    Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation. IEEE Transactions on Medical Imaging, 39(6):1856– 1867, 2019. 1, 2, 6, 7

    2019

  30. [38]

    Vision mamba: Efficient visual representation learning with bidirectional state space model

    Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417, 2024. 2

    2024 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.