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REVIEW 4 major objections 6 minor 54 references

{S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read S3-Mamba reports 77.13% small-lesion mIoU on ISIC2018, beating all compared models.

desk verdict A genuinely useful small-lesion segmentation architecture backed by consistent public-dataset gains, but the curriculum learning formulation is underspecified to the point of being unreproducible from the text. read the letter →

arxiv 2412.14546 v1 pith:F4REWZPR submitted 2024-12-19 cs.CV

Gui Wang , Yuexiang Li , Wenting Chen , Meidan Ding , Wooi Ping Cheah , Rong Qu , Jianfeng Ren , Linlin Shen This is my paper · ORCID
classification cs.CV
keywords smalllesionsegmentationMambastatespacemodelU-Netarchitecturetensor-basedattentioncurriculumlearningmulti-scalefeaturefusionmedicalimage
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

Small lesions are the earliest visible sign of many diseases, but they occupy only a few percent of an image and are easily washed out by down-sampling in standard segmentation networks. This paper proposes S3-Mamba, a Mamba-based U-shaped model that attacks the problem on three fronts: an Enhanced Visual State Space block that preserves local detail through residual connections and channel reweighting, a tensor-based multi-scale attention that fuses image, prediction, and edge features, and a curriculum-learning training schedule that gradually moves from easy to small, difficult lesions. Across ISIC2018, CVC-ClinicDB, and a private lymph-node MRI dataset, the reported gains are concentrated exactly where the authors intend: small-lesion mIoU on ISIC2018 reaches 77.13%, up from 64.04% for the VM-UNet baseline, and the model reaches 61.19% mIoU on the Lymph dataset. The design principle—preserve fine details rather than model long-range context alone—is what would generalize to other tasks where small structures carry clinical information.

What carries the argument

The load-bearing machinery is the EnVSSBlock and the TCMA bridge, both organized around keeping multi-scale information alive. EnVSSBlock edits VMamba's VSSBlock by removing the depthwise convolution, adding two residual connections, and inserting a squeeze-and-excitation-style channel reweighting (EnCFBlock); this is what preserves fine detail through the encoder and decoder. TCMA builds three patch scales from embedded input image features, intermediate per-class prediction features, and Sobel edge features, then combines them with a tensor product that sums over patch positions and uses an MLP to produce dynamic weights, finally modulating each decoder feature map. The training strategy adds a regularized curriculum that reweights samples by lesion size and loss, shifting focus from easy to small lesions. Ablations indicate the architecture modules carry most of the improvement, with curriculum learning adding a smaller increment.

What would settle it

Differentiating the stated curriculum objective with respect to the sample weights shows that the squared-weight regularizer forces the minimizer to $v_i=0$ for every $i$, so the training rule that produced the CL ablation columns cannot be the one written in Eqs. (6)-(7); a decisive check is to re-run the ISIC2018 small-lesion split with a fixed lesion-size weighting and no dynamic update, and see whether the 77.13% mIoU survives.

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Extended reading notes

Core claim

The central claim is that a Mamba-based segmentation network can be made small-lesion-sensitive without sacrificing efficiency by editing the visual state space block itself. Concretely, S3-Mamba removes the depthwise convolution from the VSS block, adds two residual connections, and inserts a squeeze-and-excitation-style EnCFBlock that reweights channels; this is the EnVSSBlock. On top of the U-Net skeleton, TCMA partitions input-image, intermediate-prediction, and Sobel edge features into patches at three scales and computes a tensor-based attention that modulates decoder features, preserving spatial detail at several granularities. A regularized curriculum-learning strategy then reweights training samples by lesion size and loss. The paper reports the top results on the small- and medium-lesion splits of ISIC2018 (77.13% and 81.36% mIoU), strong results on CVC-ClinicDB, and the best numbers on the private Lymph dataset (61.19% mIoU, 75.93% DSC), with ablations attributing the gain to all three components and the full model needing only 27.58G FLOPs and 4.64M parameters.

Load-bearing premise

The curriculum-learning contribution depends on an update rule that is not the one written down: minimizing Eqs. (6)-(7) over the sample weights would set every $v_i$ to zero and halt training, so the claimed curriculum gains rely on an unstated different optimization scheme.

Editorial extensions

If this is right

  • On ISIC2018, S3-Mamba reaches 77.13% mIoU for small lesions and 81.36% for medium lesions, beating all nine compared models on the small-lesion split.
  • On the private Lymph dataset of ultra-small lesions, S3-Mamba reaches 61.19% mIoU and 75.93% DSC, about nine mIoU points above VM-UNet, with visibly fewer false positives in the shown cases.
  • Ablations on ISIC2018 show small-lesion mIoU climbing from 64.04% at baseline to 70.02% with EnCF+CL, 72.23% with TCMA+CL, 74.89% with TCMA+EnCF, and 77.13% for the full model, so each component contributes.
  • The full model uses 27.58G FLOPs and 4.64M parameters, making it lighter than VM-UNet (39.77G FLOPs, 6.4M parameters) while scoring higher DSC on Lymph.
  • The paper attributes the gains to retaining local detail, multi-scale fusion, and size-aware training, not to any single module; the largest architectural jump comes from combining TCMA and EnVSSBlock.

Reading between the lines

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

  • Editorial inference: the stated curriculum objective cannot be minimized as written, so the reader should treat the curriculum's measured contribution as evidence for an unspecified greedy weighting scheme, not for the regularized objective in Eqs. (6)-(7).
  • Editorial inference: since the architecture components (EnVSSBlock + TCMA) already lift small-lesion mIoU from 64.04% to 74.89% on ISIC2018, most of the reported gain may be attainable without curriculum learning; a direct ablation with EnVSSBlock+TCMA plus plain lesion-size weighting would separate the causes.
  • Editorial inference: TCMA's use of intermediate prediction features and edge maps ties it to having a reasonably good initial decoder; on extremely low-contrast modalities the predicted-mask signal may be too noisy to provide useful attention, so the method's advantage may shrink when lesions are near-invisible.
  • Editorial inference: the small/medium/large split is by pixel-percentile within each dataset, so 'small lesion' is relative to dataset distribution, not a clinical size; the model's edge over other methods could change under a clinically defined diameter threshold.
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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 S3-Mamba, a Mamba-based U-Net for segmenting small lesions, with three main contributions: an Enhanced Visual State Space (EnVSS) block that adds channel-wise attention and residual connections to a VSS block; a Tensor-based Cross-feature Multi-scale Attention (TCMA) that fuses input, edge, and intermediate prediction features at multiple patch scales; and a regularized curriculum learning strategy with a Difficulty Measurer and Training Scheduler. The model is evaluated on ISIC2018, CVC-ClinicDB, and a private Lymph dataset, reporting large improvements in small-lesion mIoU and DSC over nine baselines, with ablation studies showing positive contributions from each component.

Significance. The core idea of addressing small-lesion segmentation through channel, spatial, and training-level mechanisms is relevant and timely. The reported gains on the two public datasets are substantial (e.g., small-lesion mIoU of 77.13 vs. 64.04 for VmUnet on ISIC2018), and the model is comparatively efficient (27.58G FLOPs, 4.64M parameters). The release of code and the use of standard public benchmarks are commendable. However, the formalization of the curriculum learning contribution is incomplete, the loss function is unspecified, and the evaluation lacks statistical validation. These issues currently prevent the paper from being fully reproducible and make the strength of the empirical claims hard to assess.

major comments (4)
  1. [Regularized Curriculum Learning Strategy, Eqs. (6)-(7)] The curriculum learning objective is not well-posed. Equation (6) is written as an optimization over φ only, with v 'determined by the Difficulty Measurer,' but no initial values or update rule for v are given. In Eq. (7), the first term depends only on l_i and not on v_i, so the only v-dependent term is the quadratic regularizer, which would drive v_i to 0 if v were an optimization variable. The prose says weights are initialized by lesion size and dynamically updated according to loss values, but this is never expressed as a concrete algorithm. Since Tables 3 and 4 attribute measurable gains to '+CL,' this missing specification is load-bearing for the claimed training-strategy contribution. Please provide a rigorous definition of the CL algorithm, including the exact mapping from losses to v, the update schedule, and the role of λ.
  2. [Implementation Details] The segmentation loss L(·) used in Eq. (6) and for computing l_i is never identified. The curriculum learning mechanism depends entirely on the loss values, so without the loss definition (e.g., cross-entropy, Dice, or a weighted combination) the reported gains from '+CL' cannot be reproduced. Please state the loss function and any class-weighting or hyperparameters.
  3. [Experimental Results, Tables 1-4] All quantitative results are single-run point estimates without error bars, confidence intervals, or significance tests. The central claim of improved small-lesion segmentation rests on differences such as 77.13 vs. 64.04 mIoU on ISIC2018, which may be substantial, but the absence of any variance measure makes it impossible to rule out split or initialization effects. Please report mean±std over at least three independent runs, or provide appropriate significance tests, for the main comparisons and ablation studies.
  4. [Proposed Method, Eqs. (1)-(4)] The tensor notation in the TCMA is underspecified. In particular, Pdi is introduced as a three-dimensional tensor, but Eq. (2) uses tensors P_O, P_I, P_E with channel/category/edge indices whose shapes are not defined, and the relation between the feature pyramid R and these three tensors is unclear. Without explicit tensor dimensions and a precise definition of how the multi-feature tensors are constructed and combined, the spatial-attention component cannot be re-implemented from the paper. Please clarify these definitions.
minor comments (6)
  1. [Table 1] In Table 1, the SwinUNETR row on ISIC2018 contains a typo, '98..93', for ACC; please correct it to '98.93'.
  2. [Related Work] The related work section contains a missing citation marked as '?' in the list of hybrid models; please insert the intended reference.
  3. [References] References (Wang, Chen, and Zhu 2021a) and (Wang, Chen, and Zhu 2021b) refer to the same survey and should be merged or differentiated.
  4. [Table 1] The category label 'Mam' in Table 1 should be 'Mamba' for consistency.
  5. [Model Complexity Analysis] The complexity analysis states that FLOPs stands for 'Floating Point Operations per Second'; the standard expansion is 'Floating Point Operations' (the rate is FLOPS). Please correct this.
  6. [Datasets] The private Lymph dataset is described only by the number of images and modalities; please add acquisition details (e.g., resolution, patient count, slice thickness) and the exact train/test split, since the largest gains are reported on this dataset.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the proposed components and benchmarks are independent of the claimed results, though the curriculum-learning objective is formally underspecified.

full rationale

S3-Mamba's central claims are architectural and empirical: EnVSSBlock, TCMA, and a curriculum-learning schedule are evaluated on held-out splits of ISIC2018, CVC-ClinicDB, and a private Lymph dataset. None of these components is fitted to the reported mIoU/DSC numbers; the small/medium/large test sets are disjoint from training data, and the ablations compare fixed model variants. The few citations to co-authors' prior work (TW-GAN, TR-GAN, Li & Shen 2018) appear in related-work surveys and are not load-bearing. The one problematic passage is the regularized curriculum-learning objective, Eqs. (6)-(7): v is described as 'determined by the Difficulty Measurer' and 'dynamically updated according to loss values', but no update rule is given, and the minimization over phi leaves the v-dependence unspecified. This is a reproducibility and well-posedness defect, not a circular reduction: the CL contribution is an empirical ablation component, and its reported gains are not derived from the equations. Therefore the derivation chain is self-contained with respect to circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central claim rests primarily on experimental comparisons. Key unstated premises include the exact training configuration for baselines, the segmentation loss function, and the reliability of the private Lymph dataset annotations. The curriculum learning objective as written has a degenerate optimum, suggesting the actual algorithm differs from the stated equations.

free parameters (1)
  • λ (curriculum learning weighting factor)
    Introduced in Eq. (7) to balance loss-ranking regularization and weight-squared regularization. No value or search range is reported.
assumptions (3)
  • domain assumption The baseline VmUnet is a fair and strong comparison, and all compared models are trained with identical data splits and augmentation.
    The paper does not report training details for the nine compared methods, so the reported improvements assume fair comparison conditions.
  • domain assumption The loss function used for training is an appropriate segmentation loss, but it is not specified in the paper.
    The paper defines the loss L(·) abstractly but never states the exact loss, which is needed to reproduce the results.
  • domain assumption The private Lymph dataset annotations by three radiologists are consistent and the dataset is representative of ultra-small lesion segmentation.
    The strongest results are on a private dataset that cannot be audited, so the validity of the ground truth is assumed.

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Cite this review

Pith. "Pith review of {S$^3$-Mamba}: Small-Size-Sensitive Mamba for Lesion Segmentation." pith.science (2026). https://pith.science/paper/F4REWZPR

@misc{pith2026241214546,
  author       = {Pith},
  title        = {Pith review of: S$^3$-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F4REWZPR}},
  note         = {Machine review of arXiv:2412.14546}
}
abstract

Small lesions play a critical role in early disease diagnosis and intervention of severe infections. Popular models often face challenges in segmenting small lesions, as it occupies only a minor portion of an image, while down\_sampling operations may inevitably lose focus on local features of small lesions. To tackle the challenges, we propose a {\bf S}mall-{\bf S}ize-{\bf S}ensitive {\bf Mamba} ({\bf S$^3$-Mamba}), which promotes the sensitivity to small lesions across three dimensions: channel, spatial, and training strategy. Specifically, an Enhanced Visual State Space block is designed to focus on small lesions through multiple residual connections to preserve local features, and selectively amplify important details while suppressing irrelevant ones through channel-wise attention. A Tensor-based Cross-feature Multi-scale Attention is designed to integrate input image features and intermediate-layer features with edge features and exploit the attentive support of features across multiple scales, thereby retaining spatial details of small lesions at various granularities. Finally, we introduce a novel regularized curriculum learning to automatically assess lesion size and sample difficulty, and gradually focus from easy samples to hard ones like small lesions. Extensive experiments on three medical image segmentation datasets show the superiority of our S$^3$-Mamba, especially in segmenting small lesions. Our code is available at https://github.com/ErinWang2023/S3-Mamba.

Figures

Figures reproduced from arXiv: 2412.14546 by the authors.

Figure 1
Figure 1. Segmentation results of CNN-based Unet (Ron [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. (a) Overview of the proposed S3 -Mamba with TCMA and EnVSSBlock. (b) Detailed architecture of the TCMA, where input image features, intermediate-layer features, and edge features are divided into patches of three different sizes. A tensor￾based attention derives the dynamic weights of these patches of three different scales, exploits their interaction, and utilizes the TCMA features to modulate the features at decod… view at source ↗
Figure 3
Figure 3. Segmentation results on the Lymph dataset. Red [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison with other models in terms of FLOPs, [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Works this paper leans on

54 extracted references · 40 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    E.; Lamarque, D.; Daul, C.; Riegler, M

    Ali, S.; Jha, D.; Ghatwary, N.; Realdon, S.; Cannizzaro, R.; Salem, O. E.; Lamarque, D.; Daul, C.; Riegler, M. A.; Anonsen, K. V.; et al. 2023. A multi-centre polyp detection and segmentation dataset for generalisability assessment. Scientific Data, 10(1): 75

    work page 2023

  4. [4]

    Azad, R.; Asadi-Aghbolaghi, M.; Fathy, M.; and Escalera, S. 2019. Bi-directional ConvLSTM U-Net with densley connected convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 10--20

    work page 2019

  5. [5]

    M.; Mucientes, M.; and Del Bimbo, A

    Bosquet, B.; Cores, D.; Seidenari, L.; Brea, V. M.; Mucientes, M.; and Del Bimbo, A. 2023. A full data augmentation pipeline for small object detection based on generative adversarial networks. Pattern Recognition, 133: 108998

    work page 2023

  6. [6]

    Cao, H.; Wang, Y.; Chen, J.; Jiang, D.; Zhang, X.; Tian, Q.; and Wang, M. 2022. Swin-unet: Unet-like pure transformer for medical image segmentation. In European Conference on Computer Vision, 205--218. Springer

    work page 2022

  7. [7]

    Cao, M.; Wang, X.; Qi, Z.; Shan, Y.; Qie, X.; and Zheng, Y. 2023. Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 22560--22570

    2023

  8. [8]

    Chen, W.; Yu, S.; Ma, K.; Ji, W.; Bian, C.; Chu, C.; Shen, L.; and Zheng, Y. 2022. TW-GAN: Topology and width aware GAN for retinal artery/vein classification. Medical Image Analysis, 77: 102340

    work page 2022

Show all 54 references
  1. [9]

    Chen, W.; Yu, S.; Wu, J.; Ma, K.; Bian, C.; Chu, C.; Shen, L.; and Zheng, Y. 2020. TR-GAN: Topology ranking GAN with triplet loss for retinal artery/vein classification. In Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, ...

    2020

  2. [10]

    K.; Mattausch, A.; Stojanovska, F.; Zimmerli, C

    de Teresa-Trueba, I.; Goetz, S. K.; Mattausch, A.; Stojanovska, F.; Zimmerli, C. E.; Toro-Nahuelpan, M.; Cheng, D. W.; Tollervey, F.; Pape, C.; Beck, M.; et al. 2023. Convolutional networks for supervised mining of molecular patterns within cellular context. Nature Methods, 20...

    2023

  3. [11]

    Du, S.; Bayasi, N.; Hamarneh, G.; and Garbi, R. 2023. AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 25--36. Springer

    2023

  4. [12]

    J.; Brodtmann, A.; Chen, C.; Cordonnier, C.; de Leeuw, F.-E.; Debette, S.; Frayne, R.; Jouvent, E.; Rost, N

    Duering, M.; Biessels, G. J.; Brodtmann, A.; Chen, C.; Cordonnier, C.; de Leeuw, F.-E.; Debette, S.; Frayne, R.; Jouvent, E.; Rost, N. S.; et al. 2023. Neuroimaging standards for research into small vessel disease—advances since 2013. The Lancet Neurology, 22(7): 602--618

    2023

  5. [13]

    M.; Hanna, J.; and Khan, F

    Fiaz, M.; Noman, M.; Cholakkal, H.; Anwer, R. M.; Hanna, J.; and Khan, F. S. 2024. Guided-attention and gated-aggregation network for medical image segmentation. Pattern Recognition, 110812

    2024

  6. [14]

    Gao, J.; Geng, X.; Zhang, Y.; Wang, R.; and Shao, K. 2024. Augmented weighted bidirectional feature pyramid network for marine object detection. Expert Systems with Applications, 237: 121688

    2024

  7. [15]

    Gao, Y.; Zhou, M.; Liu, D.; Yan, Z.; Zhang, S.; and Metaxas, D. N. 2022. A data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark. arXiv preprint arXiv:2203.00131

    2022 arXiv

  8. [16]

    Garcea, F.; Serra, A.; Lamberti, F.; and Morra, L. 2023. Data augmentation for medical imaging: A systematic literature review. Computers in Biology and Medicine, 152: 106391

    2023

  9. [17]

    F.; Miller, G.; Moen, E.; Kong, A.; Kagel, A.; Dougherty, T.; Fullaway, C

    Greenwald, N. F.; Miller, G.; Moen, E.; Kong, A.; Kagel, A.; Dougherty, T.; Fullaway, C. C.; McIntosh, B. J.; Leow, K. X.; Schwartz, M. S.; et al. 2022. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. N...

    2022

  10. [18]

    Gu, A.; and Dao, T. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752

    2023 arXiv

  11. [19]

    He, A.; Wang, K.; Li, T.; Du, C.; Xia, S.; and Fu, H. 2023. H2Former: An efficient hierarchical hybrid transformer for medical image segmentation. IEEE Transactions on Medical Imaging, 42(9): 2763--2775

    2023

  12. [20]

    V.; Sj \"o gren, R.; Aubreville, M.; Koos, K.; and Horvath, P

    Hirling, D.; Tasnadi, E.; Caicedo, J.; Caroprese, M. V.; Sj \"o gren, R.; Aubreville, M.; Koos, K.; and Horvath, P. 2024. Segmentation metric misinterpretations in bioimage analysis. Nature Methods, 21(2): 213--216

    2024

  13. [21]

    Hu, J.; Shen, L.; and Sun, G. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132--7141

    2018

  14. [22]

    Huang, H.; Chen, Z.; Zou, Y.; Lu, M.; Chen, C.; Song, Y.; Zhang, H.; and Yan, F. 2024. Channel prior convolutional attention for medical image segmentation. Computers in Biology and Medicine, 178: 108784

    2024

  15. [23]

    B.; and Mahjoub, M

    Jegham, I.; Alouani, I.; Khalifa, A. B.; and Mahjoub, M. A. 2023. Deep learning-based hard spatial attention for driver in-vehicle action monitoring. Expert Systems with Applications, 219: 119629

    2023

  16. [24]

    H.; Riegler, M

    Jha, D.; Smedsrud, P. H.; Riegler, M. A.; Johansen, D.; De Lange, T.; Halvorsen, P.; and Johansen, H. D. 2019. Resunet++: An advanced architecture for medical image segmentation. In 2019 IEEE International Symposium on Multimedia (ISM), 225--2255. IEEE

    2019

  17. [25]

    Kong, Y.; Liu, L.; Wang, J.; and Tao, D. 2021. Adaptive curriculum learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 5067--5076

    2021

  18. [26]

    Li, Y.; and Shen, L. 2018. Skin lesion analysis towards melanoma detection using deep learning network. Sensors, 18(2): 556

    2018

  19. [27]

    Liu, Y.; Tian, Y.; Zhao, Y.; Yu, H.; Xie, L.; Wang, Y.; Ye, Q.; and Liu, Y. 2024. Vmamba: Visual state space model 2024. arXiv preprint arXiv:2401.10166

    2024 arXiv

  20. [28]

    Luo, X.; Liu, C.; Wong, W.; Wen, J.; Jin, X.; and Xu, Y. 2023. MVCINN: multi-view diabetic retinopathy detection using a deep cross-interaction neural network. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, 8993--9001

    2023

  21. [29]

    Luo, X.; Xu, Q.; Wang, Z.; Huang, C.; Liu, C.; Jin, X.; and Zhang, J. 2024. A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading. IEEE Journal of Biomedical and Health Informatics

    2024

  22. [30]

    M.; Li, W.; Li, S.; Liu, F.; and Xu, G

    Luvembe, A. M.; Li, W.; Li, S.; Liu, F.; and Xu, G. 2023. Dual emotion based fake news detection: A deep attention-weight update approach. Information Processing & Management, 60(4): 103354

    2023

  23. [31]

    Ma, J.; Li, F.; and Wang, B. 2024. U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722

    2024 arXiv

  24. [32]

    Ma, J.; and Wang, B. 2023. Towards foundation models of biological image segmentation. Nature Methods, 20(7): 953--955

    2023

  25. [33]

    S.; and Shi, H

    Mei, Y.; Fan, Y.; Zhang, Y.; Yu, J.; Zhou, Y.; Liu, D.; Fu, Y.; Huang, T. S.; and Shi, H. 2023. Pyramid attention network for image restoration. International Journal of Computer Vision, 131(12): 3207--3225

    2023

  26. [34]

    Miao, J.; Zhou, S.-P.; Zhou, G.-Q.; Wang, K.-N.; Yang, M.; Zhou, S.; and Chen, Y. 2023. SC-SSL: Self-correcting Collaborative and Contrastive Co-training Model for Semi-Supervised Medical Image Segmentation. IEEE Transactions on Medical Imaging

    2023

  27. [35]

    Milletari, F.; Navab, N.; and Ahmadi, S.-A. 2016. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D vision (3DV), 565--571. Ieee

    2016

  28. [36]

    N.; Mohammed, H

    Omeroglu, A. N.; Mohammed, H. M.; Oral, E. A.; and Aydin, S. 2023. A novel soft attention-based multi-modal deep learning framework for multi-label skin lesion classification. Engineering Applications of Artificial Intelligence, 120: 105897

    2023

  29. [37]

    B.; Wahid, A.; Khan, M

    Qureshi, I.; Yan, J.; Abbas, Q.; Shaheed, K.; Riaz, A. B.; Wahid, A.; Khan, M. W. J.; and Szczuko, P. 2023. Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends. Information Fusion, 90: 316--352

    2023

  30. [38]

    Ronneberger, O.; Fischer, P.; and Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, part II...

    2015

  31. [39]

    Ruan, J.; and Xiang, S. 2024. Vm-unet: Vision mamba unet for medical image segmentation. arXiv preprint arXiv:2402.02491

    2024 arXiv

  32. [40]

    M.; Maaz, M.; Rasheed, H.; Khan, S.; Yang, M.-H.; and Khan, F

    Shaker, A. M.; Maaz, M.; Rasheed, H.; Khan, S.; Yang, M.-H.; and Khan, F. S. 2024. UNETR++: delving into efficient and accurate 3D medical image segmentation. IEEE Transactions on Medical Imaging

    2024

  33. [41]

    K.; Rai, S

    Singh, D. K.; Rai, S. N.; Joseph, K.; Saluja, R.; Balasubramanian, V. N.; Arora, C.; Subramanian, A.; and Jawahar, C. 2021. Order: Open world object detection on road scenes. In Proc. NeurIPS Workshops, volume 1, 3

    2021

  34. [42]

    R.; Landman, B.; Xu, D.; Nath, V.; and Hatamizadeh, A

    Tang, Y.; Yang, D.; Li, W.; Roth, H. R.; Landman, B.; Xu, D.; Nath, V.; and Hatamizadeh, A. 2022. Self-supervised pre-training of swin transformers for 3d medical image analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 20730--20740

    2022

  35. [43]

    Valanarasu, J. M. J.; Oza, P.; Hacihaliloglu, I.; and Patel, V. M. 2021. Medical transformer: Gated axial-attention for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, Se...

    2021

  36. [44]

    Wang, J.; Lai, C.; Wang, Y.; and Zhang, W. 2024 a . EMAT: Efficient feature fusion network for visual tracking via optimized multi-head attention. Neural Networks, 172: 106110

    2024

  37. [45]

    Wang, P.; Ma, Z.; Dong, B.; Liu, X.; Ding, J.; Sun, K.; and Chen, Y. 2024 b . Generative data augmentation by conditional inpainting for multi-class object detection in infrared images. Pattern Recognition, 153: 110501

    2024

  38. [46]

    Wang, X.; Chen, Y.; and Zhu, W. 2021 a . A survey on curriculum learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9): 4555--4576

    2021

  39. [47]

    Wang, X.; Chen, Y.; and Zhu, W. 2021 b . A survey on curriculum learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9): 4555--4576

    2021

  40. [48]

    Wang, Y.; Gan, W.; Yang, J.; Wu, W.; and Yan, J. 2019. Dynamic curriculum learning for imbalanced data classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 5017--5026

    2019

  41. [49]

    Xing, Z.; Ye, T.; Yang, Y.; Liu, G.; and Zhu, L. 2024. Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation. arXiv preprint arXiv:2401.13560

    2024 arXiv

  42. [50]

    Xu, Q.; Luo, X.; Huang, C.; Liu, C.; Wen, J.; Wang, J.; and Xu, Y. 2024. HACDR-Net: Heterogeneous-Aware Convolutional Network for Diabetic Retinopathy Multi-Lesion Segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, 6342--6350

    2024

  43. [51]

    Zhang, J.; Ren, J.; Zhang, Q.; Liu, J.; and Jiang, X. 2023. Spatial Context-Aware Object-Attentional Network for Multi-Label Image Classification. IEEE Transactions on Image Processing, 32: 3000--3012

    2023

  44. [52]

    Zhang, J.; Yang, X.; He, W.; Ren, J.; Zhang, Q.; Zhao, Y.; Bai, R.; He, X.; and Liu, J. 2024 a . Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, 410--418

    2024

  45. [53]

    Zhang, Y.; Ye, M.; Zhu, G.; Liu, Y.; Guo, P.; and Yan, J. 2024 b . FFCA-YOLO for small object detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing

    2024

  46. [54]

    Zhu, L.; Liao, B.; Zhang, Q.; Wang, X.; Liu, W.; and Wang, X. 2024. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417

    2024 arXiv

Pith tools

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