Pith. sign in

REVIEW 4 major objections 5 minor 35 references

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read InceptionMamba claims state-of-the-art microscopic and skin-lesion segmentation with about 4.85x fewer GFLOPs and 1.45x fewer parameters than the previous best method.

desk verdict InceptionMamba is a plausible, efficient U-Net variant whose headline SOTA/efficiency numbers rest on protocol-matched comparisons and reproducible FLOPs that the paper does not yet supply. read the letter →

arxiv 2506.12208 v1 pith:B6YRUAI6 submitted 2025-06-13 cs.CV

Daniya Najiha Abdul Kareem , Abdul Hannan , Mubashir Noman , Jean Lahoud , Mustansar Fiaz , Hisham Cholakkal This is my paper
classification cs.CV
keywords medicalimagesegmentationmicroscopicMambaselectivestatespacemodelfeaturecalibrationInceptionmoduleefficientdeeplearningskinlesion
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

InceptionMamba is a medical image segmentation network whose central claim is that multi-stage feature enrichment plus a hybrid Inception-Mamba module reaches state-of-the-art accuracy on four benchmarks while using about 4.85 times fewer GFLOPs and 1.45 times fewer parameters than the previous best method, GA2-Net. The authors' premise is that CNNs and transformers struggle with cluttered backgrounds, blurred boundaries, and large shape and size variation in microscopic images, and that a selective state-space model can supply global context at lower cost. The proposed pipeline enhances features from the first three ResNet stages with a feature calibration module that emphasizes boundary detail, fuses them through a bottleneck containing the Inception Mamba module, and decodes with a lightweight stack that avoids dense skip connections. The reported results on SegPC21, GlaS, ISIC2017, and ISIC2018 support the claim that efficiency and accuracy do not have to trade off. If the comparisons are sound, the practical consequence is that high-accuracy medical segmentation could run on much smaller computational budgets.

What carries the argument

The Inception Mamba module (IMM) is the central object: input features are split channel-wise into several branches, with an identity branch, depthwise convolution branches using square and rectangular kernels of different receptive fields, and a Mamba selective state-space branch, whose outputs are concatenated. The Feature Calibration Module (FCM) smooths features by stride-2 depthwise convolution followed by upsampling, then uses multiplication and subtraction of the original and smoothed maps to emphasize blob regions and fine boundary details. The bottleneck block applies FCM to each of three backbone stages, concatenates and intermixes them with a 1x1 convolution, passes the result through IMM, and adds the IMM output back as a residual. The decoder drops dense skip connections and instead cascades convolutions and upsampling with IMM in the middle, fusing only the stem features at the end.

What would settle it

Run InceptionMamba and GA2-Net with identical, scripted preprocessing, augmentation, optimizer, epoch count, and inference on SegPC21 and ISIC2018; if GA2-Net's Dice and IoU then match or beat InceptionMamba, or if the FLOPs gap disappears under the same input resolution, the paper's performance and efficiency claims fail. The single-model, no-ensemble GlaS comparison would be the simplest decisive check.

Watch

Extended reading notes

Core claim

The paper's central claim is that channel-wise fusion of Inception-style depthwise convolutions and a Mamba selective state-space block, applied to boundary-enhanced multi-stage features, produces more accurate segmentation of cells and skin lesions than prior CNNs, transformers, and Mamba hybrids, at a fraction of the compute. On SegPC21 it reports 92.56 Dice and 92.37 IoU with a ResNet50 backbone, and 92.84 Dice and 92.63 IoU with a PVT-V2-B2 backbone at 4.86 GFLOPs, against 92.49 Dice and 92.32 IoU for GA2-Net. It also reports lower HD95 boundary error than GA2-Net (1.92 vs 3.87 on SegPC21) and top scores on GlaS, ISIC2017, and ISIC2018. The authors attribute the gains to the feature calibration module, which uses downsampling-upsampling smoothing to separate fine boundary detail from blob structure, and to the Inception Mamba module, whose split branches let the model see multiple kernel shapes and a global selective-scan context simultaneously. The efficiency comes from replacing dense skip connections with a simple decoder and from feeding the Mamba block only a slice of the channels.

Load-bearing premise

The central claim assumes every compared method was trained and tested under the same protocol, meaning the same splits, preprocessing (nucleus cropping and 224x224 resizing), augmentation, loss, and the five-fold ensemble used for GlaS, because the paper follows prior papers' settings rather than retraining all baselines under one pipeline.

Editorial extensions

If this is right

  • If the comparisons are fair, InceptionMamba gives practitioners a segmentation model that is roughly five times cheaper than the strongest prior method, making high-accuracy cell and lesion segmentation feasible on lighter hardware.
  • The ablation table implies FCM and IMM are complementary: FCM adds boundary detail, IMM adds multi-context features, and removing either one costs about one Dice point.
  • The decoder's success without dense skip connections suggests that heavy U-Net-style skip wiring is not necessary once the bottleneck has fused multi-stage features.
  • Using a PVT-V2 backbone over ResNet50 improves accuracy further and lowers GFLOPs, indicating the framework benefits from a stronger encoder rather than needing custom ones.
  • Mamba-based global modeling can be restricted to a channel subset and still outperform full transformer encoders, pointing to a cheaper design regime for medical imaging.

Reading between the lines

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

  • Extension: the FCM's multiply-and-subtract treatment of original versus smoothed features is a generic low- and high-frequency separation, so the same calibration could be dropped into other dense-prediction heads, such as detection or instance segmentation, without the Mamba branch.
  • Extension: because the Mamba block sees only one channel subset, the paper implicitly suggests that a partial global context is enough for these datasets; a direct test would be varying the Mamba channel fraction and measuring Dice versus FLOPs, which the paper does not report.
  • Extension: the reported gains rest on protocol transfer from earlier papers, so re-running all baselines under one scripted pipeline would settle whether the roughly fivefold efficiency advantage is robust or partly an artifact of different preprocessing and ensembling choices.
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 / 5 minor

Summary. The paper proposes InceptionMamba, a hybrid CNN-Mamba segmentation architecture for microscopic and dermoscopic medical images. The model combines a ResNet backbone with a proposed Inception Mamba Module (IMM) and Feature Calibration Module (FCM) in a bottleneck block, plus a lightweight decoder without dense skip connections. The authors report state-of-the-art Dice/IoU on SegPC21, GlaS, ISIC2017, and ISIC2018, and claim approximately 4.85x fewer GFLOPs and 1.45x fewer parameters than the previous best method GA2-Net. The paper includes quantitative comparisons, ablations on SegPC21, qualitative visualizations, and HD95 evaluations.

Significance. If the reported results are protocol-matched and reproducible, the paper offers a practical efficiency-accuracy trade-off for medical segmentation, an area where such trade-offs matter clinically. The ablation study in Table 4 is internally consistent and supports the individual contributions of FCM and IMM. The qualitative results and HD95 scores provide complementary evidence for boundary-quality improvements. However, the central SOTA and efficiency claims are not yet independently verifiable because the comparison protocol across baselines is not established, errors bars are missing on two of the four datasets, and no code or checkpoints are provided. The contribution is incremental (a combination of depth-wise inception-style convolutions with Mamba blocks) but the efficiency claim is substantial if confirmed.

major comments (4)
  1. [Section 3.1, Tables 1-3] The headline SOTA claim depends on comparing against published baseline numbers that may come from different protocols. The paper fixes the input resolution at 224x224 and states that it follows [1,8] and UCTransNet [27] for splits and augmentation, but it does not verify that the quoted results for GA2-Net, UDTransNet, LKMUNet, U-Mamba, and other baselines were obtained with the same nucleus-cropping for SegPC21, the same resizing, the same augmentation, or the same inference procedure. The margins over the previous best are 0.07 Dice on SegPC21 and 0.65 Dice on ISIC2018, both reported without error bars, so a modest protocol shift could change the ranking. Please report multi-run statistics with standard deviations and, ideally, rerun the strongest baselines under the same code base, or provide explicit evidence that all compared numbers are protocol-matched.
  2. [Section 3.1, Table 2] For the GlaS dataset, the paper states that, following [8,27], it performs three runs of 5-fold cross-validation and uses an ensemble of the five models at inference. Table 2 does not state which of the cited baselines also used ensembling, and several baselines report standard deviations of about 0.4-0.7 Dice. Since the reported Dice lead over SA2-Net is 0.50, the advantage could be attributable to the ensemble rather than to the proposed architecture. Please report single-model results for InceptionMamba as well, or clearly identify the inference protocol used for every row in Table 2.
  3. [Section 2.3, Tables 1 and 4] The efficiency claim of 4.85x fewer GFLOPs than GA2-Net is only meaningful if all FLOPs are counted with the same conventions and at the same input resolution. The paper reports GFLOPs values but does not describe the counting tool, the inclusion or exclusion of the backbone, or how the SSM scan and depth-wise operations are counted. The ablation table shows GFLOPs values that vary in ways that are not fully explained (e.g., 'IMM with only Mamba' increasing GFLOPs beyond the full IMM). Without code/checkpoints and a stated FLOPs-counting methodology, the efficiency comparison cannot be independently verified. Please release code and provide a detailed FLOPs profile for each component.
  4. [Section 3.4, Tables 4 and 5] The architectural choices that define the final model are selected using the SegPC21 test set. The choice of identity over pooling in IMM, the position of IMM in the decoder, and the number of backbone stages are all validated on the same test set where the final SOTA numbers are reported. This selection-on-test-set procedure can inflate the reported performance. Please add a validation-based model selection protocol, a separate unseen test split, or report the variance across multiple seeds and folds so the reader can assess selection effects.
minor comments (5)
  1. [Table 1] The table formatting has several textual artifacts, such as '113.1391.10' and 'Dice (%)IoU (%)', where missing spacing makes values run together; please fix the table typesetting.
  2. [Section 3.2] The HD95 scores for the proposed method are reported without a specification of the distance metric implementation (e.g., symmetric Hausdorff distance, number of surface points sampled) and without HD95 values for all baselines in Tables 1 and 3, so the comparison is incomplete.
  3. [Section 2.4] The description of the FCM input resolutions is ambiguous: the text says the first-stage features are downsampled by a factor of four and the second-stage features by a factor of two before the FCM, but Figure 4 shows a single stride-2 depth-wise convolution; please clarify how these factors are applied.
  4. [Section 3.1] The statement 'we follow [1,8]' for SegPC21 and ISIC2018 is vague because [1] is a general review and [8] is SA2-Net; please list the exact split, preprocessing, and augmentation details for each dataset.
  5. [Section 2.3, Table 4] The identity branch is said to be 'empirically observed' to outperform the max-pooling branch used in Inception blocks, but no direct ablation comparing identity with max-pooling within the proposed IMM is shown; adding such an ablation would strengthen the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SOTA and efficiency claims are empirical benchmark results, not reductions to fitted inputs or self-citations.

full rationale

The paper contains no mathematical derivation chain to walk: the method is an architectural composition of ResNet features, a Feature Calibration Module, an Inception Mamba Module, and a lightweight decoder, validated by benchmark tables and ablations. The central claim is an empirical performance comparison on SegPC21, GlaS, ISIC2017, and ISIC2018, which are external public datasets; the reported numbers are not obtained by fitting a parameter to a target and then re-predicting that same target. The authors' prior works SA2-Net [8] and GA2-Net [9] are used as baselines and as references for training protocol, not as load-bearing justifications that define or prove the InceptionMamba architecture, and no uniqueness theorem or ansatz is imported from those citations. No known result is renamed under new coordinates. The main caveat, protocol comparability of prior published numbers (especially the GlaS five-model ensemble and the GFLOPs counting convention), is an external-validity and reproducibility concern rather than circularity: even if the comparisons were protocol-mismatched, the proposed architecture's performance would not be true by construction. Likewise, using SegPC21 for ablations and final reporting carries a mild selection risk but does not make the result definitionally equivalent to its inputs. Therefore no specific circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 2 invented entities

The central claim rests on standard deep learning assumptions (transferability of pretrained features, validity of benchmarks, and the effectiveness of Mamba for 2D segmentation) plus two newly introduced modules whose utility is only established by in-paper ablations. The free parameters are hand-chosen architectural details that influence the reported results.

free parameters (4)
  • Input resolution = 224x224
    All models are trained and evaluated at this resolution; the GFLOPs comparison with prior methods assumes the same resolution was used for their reported numbers.
  • IMM decoder placement = middle of decoder
    Selected via an ablation study on SegPC21 (Table 5); the choice affects final Dice by up to 0.35 points and is a hand-picked design decision.
  • Number of encoder stages used = stem plus stages 1-3
    The fourth backbone stage is omitted because the authors observed it is 'not profitable' for microscopic images; no ablation study is provided for this choice.
  • FCM downsampling factors = 4x for stage 1, 2x for stage 2
    Hand-set factors used to align feature resolutions before fusion in the bottleneck; no sensitivity analysis is reported.
assumptions (4)
  • domain assumption ImageNet-pretrained ResNet50 features transfer to microscopic and dermoscopic images.
    The method uses a ResNet50 backbone pretrained on ImageNet; the assumption that these features are useful for medical images is not independently justified.
  • domain assumption Mamba selective scan state space model is effective for 2D image segmentation when applied to flattened image features.
    Mamba was designed for 1D sequences; its extension to 2D images is assumed based on prior work (U-Mamba, LKMUNet).
  • domain assumption The benchmark datasets and their ground truth provide a valid measure of segmentation quality.
    The paper relies on public datasets (SegPC21, GlaS, ISIC2017/18) and Dice/IoU metrics as the ground truth for the state-of-the-art claim.
  • ad hoc to paper The feature calibration operation (multiplying original and smoothed features, and subtracting them) highlights blob regions and fine details as intended.
    This mechanism is introduced by the authors without a formal or empirical justification beyond the overall ablation; its interpretation as frequency separation is an assertion.
invented entities (2)
  • Inception Mamba Module (IMM)
    purpose: A multi-branch module that splits input channels and processes them with identity, depth-wise convolutions of differing kernel sizes, and a Mamba block to capture multi-scale context.
    The module's contribution is demonstrated only through in-paper ablations on SegPC21; there is no external benchmark or theoretical analysis that independently validates it.
  • Feature Calibration Module (FCM)
    purpose: Enhances boundary information by downsampling and upsampling feature maps, then subtracting and multiplying the original with the smoothed version.
    The module is validated only by the ablation in Table 4; its claimed effect on boundary regions is not independently verified outside the paper's own experiments.

how reviews work

0 comments
Cite this review

Pith. "Pith review of InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation." pith.science (2026). https://pith.science/paper/B6YRUAI6

@misc{pith2026250612208,
  author       = {Pith},
  title        = {Pith review of: InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B6YRUAI6}},
  note         = {Machine review of arXiv:2506.12208}
}
read the original abstract

Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreover, their reliance on the availability of large datasets for improved performance, along with the high computational cost, limit their practicality. To address these issues, we propose an efficient framework for the segmentation task, named InceptionMamba, which encodes multi-stage rich features and offers both performance and computational efficiency. Specifically, we exploit semantic cues to capture both low-frequency and high-frequency regions to enrich the multi-stage features to handle the blurred region boundaries (e.g., cell boundaries). These enriched features are input to a hybrid model that combines an Inception depth-wise convolution with a Mamba block, to maintain high efficiency and capture inherent variations in the scales and shapes of the regions of interest. These enriched features along with low-resolution features are fused to get the final segmentation mask. Our model achieves state-of-the-art performance on two challenging microscopic segmentation datasets (SegPC21 and GlaS) and two skin lesion segmentation datasets (ISIC2017 and ISIC2018), while reducing computational cost by about 5 times compared to the previous best performing method.

Figures

Figures reproduced from arXiv: 2506.12208 by the authors.

Figure 1
Figure 1. Comparison of our method with state￾of-the-art models based on Dice score, parameters, and GFLOPs on the SegPC21 dataset. Our method performs favorably compared to the state-of-the￾art GA2-Net while requiring 4.85× fewer GFLOPs and 1.45× fewer parameters. Earlier segmentation methods in the medical domain were composed of convolutional neural networks (CNNs) in an encoder-decoder fashion. UNet [21], introduced a dec… view at source ↗
Figure 2
Figure 2. Illustration of the overall architecture of our proposed framework. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Inception Mamba module (IMM) takes input features Xn, splits them channel-wise, and utilize various convolution kernels and state space mod￾ule to extract multi-contextual features Xc for better segmentation performance as depicted above. 2.3 Inception Mamba Module The Inception Mamba module (IMM) is illus￾trated in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Illustration of the feature cali￾bration module. Feature maps Fi are first down-sampled using depth-wise convolu￾tion followed by an upsampling opera￾tion. Then, we multiply features Fi with Fˆ i to highlight blob regions. Similarly, subtraction of Fi and Fˆ i is perfo…
Figure 5
Figure 5. Figure 5: Qualitative comparison of the proposed method [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparisons of different methods on the [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Illustration of the impact of our contributions. (a) is the input to the model, (b) [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

35 extracted references · 21 canonical work pages

  1. [24]

    Rethinking the inception architecture for computer vision

    Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016

    2016

  2. [27]

    Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer

    Haonan Wang, Peng Cao, Jiaqi Wang, and Osmar R Zaiane. Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pages 2441– 2449, 2022

    work page 2022

  3. [1]

    Medical image segmentation re- view: The success of u-net

    Reza Azad, Ehsan Khodapanah Aghdam, and et al. Medical image segmentation re- view: The success of u-net. IEEE Transactions on Pattern Analysis and Machine In- telligence, 2024

    work page 2024

  4. [2]

    Dense dilated inception network for medical image segmentation

    Surayya Ado Bala and Shri Kant. Dense dilated inception network for medical image segmentation. international Journal of Advanced Computer Science and Applications, 11(11), 2020

    work page 2020

  5. [3]

    Swin-unet: Unet-like pure transformer for medical image segmenta- tion

    Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. Swin-unet: Unet-like pure transformer for medical image segmenta- tion. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III, pages 205–218. Springer, 2023

    work page 2022

  6. [4]

    Transunet: Transformers make strong encoders for medical 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 medical image segmentation. arXiv preprint arXiv:2102.04306, 2021

    arXiv 2021

  7. [5]

    Skin lesion analysis toward melanoma detection 2018: A chal- lenge hosted by the international skin imaging collaboration (isic)

    Noel Codella et al. Skin lesion analysis toward melanoma detection 2018: A chal- lenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1902.03368, 2019

    arXiv 2018

  8. [6]

    Noel CF Codella et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the inter- national skin imaging collaboration (isic). In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pages 168–172. IEEE, 2018. STUDENT, PROF, COLLABORA TOR: BMVC AUTH...

    work page 2017

Show all 35 references
  1. [7]

    Imagenet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009

    2009

  2. [8]

    Sa2- net: Scale-aware attention network for microscopic image segmentation.arXiv preprint arXiv:2309.16661, 2023

    Mustansar Fiaz, Moein Heidari, Rao Muhammad Anwar, and Hisham Cholakkal. Sa2- net: Scale-aware attention network for microscopic image segmentation.arXiv preprint arXiv:2309.16661, 2023

    2023 arXiv

  3. [9]

    Guided-attention and gated-aggregation network for medical image segmentation

    Mustansar Fiaz, Mubashir Noman, and et al. Guided-attention and gated-aggregation network for medical image segmentation. Pattern Recognition, page 110812, 2024

    2024

  4. [10]

    Mamba: Linear-time sequence modeling with selective state spaces

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

    2023 arXiv

  5. [11]

    Segpc-2021: Seg- mentation of multiple myeloma plasma cells in microscopic images

    Anubha Gupta, Ritu Gupta, Shiv Gehlot, and Shubham Goswami. Segpc-2021: Seg- mentation of multiple myeloma plasma cells in microscopic images. IEEE Dataport, 1 (1):1, 2021

    2021

  6. [12]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016

    2016

  7. [13]

    Missformer: An effective medical image segmentation transformer

    Xiaohong Huang, Zhifang Deng, Dandan Li, and Xueguang Yuan. Missformer: An effective medical image segmentation transformer. arXiv preprint arXiv:2109.07162, 2021

    2021 arXiv

  8. [14]

    Multiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation

    Nabil Ibtehaz and M Sohel Rahman. Multiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation. Neural networks, 121:74–87, 2020

    2020

  9. [15]

    Medical image segmentation using directional window attention

    Daniya Najiha Abdul Kareem, Mustansar Fiaz, Noa Novershtern, and Hisham Cholakkal. Medical image segmentation using directional window attention. arXiv preprint arXiv:2406.17471, 2024

    2024 arXiv

  10. [16]

    Analyzing microbial pop- ulation heterogeneity—expanding the toolbox of microfluidic single-cell cultivations

    Markus Leygeber, Dorina Lindemann, Christian Carsten Sachs, Eugen Kaganovitch, Wolfgang Wiechert, Katharina Nöh, and Dietrich Kohlheyer. Analyzing microbial pop- ulation heterogeneity—expanding the toolbox of microfluidic single-cell cultivations. Journal of molecular biology,...

    2019

  11. [17]

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

    Ailiang Lin, Bingzhi Chen, Jiayu Xu, Zheng Zhang, Guangming 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

    2022

  12. [18]

    Swin-umamba: Mamba-based unet with imagenet-based pretraining.arXiv preprint arXiv:2402.03302, 2024

    Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Yizhou Yu, Yong Liang, Guangming Shi, Shaoting Zhang, Hairong Zheng, and Shanshan Wang. Swin-umamba: Mamba-based unet with imagenet-based pretraining.arXiv preprint arXiv:2402.03302, 2024

    2024 arXiv

  13. [19]

    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. 12 STUDENT, PROF, COLLABORA TOR: BMVC AUTHOR GUIDELINES

    2024 arXiv

  14. [20]

    Attention u-net: Learning where to look for the pancreas

    Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazu- nari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999, 2018

    2018 arXiv

  15. [21]

    U-net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer- Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Ger- many, October 5-9, 2015, Proceedings, Pa...

    2015

  16. [22]

    Gland segmentation in colon histology images: The glas challenge contest

    Korsuk Sirinukunwattana, Josien PW Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J Matuszewski, Elia Bruni, Urko Sanchez, et al. Gland segmentation in colon histology images: The glas challenge contest. Med- ical image analysis, 35:489–502, 2017

    2017

  17. [23]

    Going deeper with convolutions

    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pat- tern recognition, pages 1–9, 2015

    2015

  18. [25]

    The fully convolutional transformer for medical image segmentation

    Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith, and Dirk Husmeier. The fully convolutional transformer for medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3660– 3669, 2023

    2023

  19. [26]

    Med- ical transformer: Gated axial-attention for medical image segmentation

    Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, and Vishal M Patel. Med- ical transformer: Gated axial-attention for medical image segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th Interna- tional Conference, Strasbourg,...

    2021

  20. [28]

    Narrowing the semantic gaps in u-net with learnable skip connections: The case of medical image segmentation

    Haonan Wang, Peng Cao, Jinzhu Yang, and Osmar Zaiane. Narrowing the semantic gaps in u-net with learnable skip connections: The case of medical image segmentation. Neural Networks, page 106546, 2024

    2024

  21. [29]

    Large kernel vision mamba unet for medical image segmentation

    Jinhong Wang, Jintai Chen, Danny Chen, and Jian Wu. Large kernel vision mamba unet for medical image segmentation. arXiv preprint arXiv:2403.07332, 2024

    2024 arXiv

  22. [30]

    U-net using stacked dilated convolutions for medical image segmentation

    Shuhang Wang, Szu-Yeu Hu, Eugene Cheah, Xiaohong Wang, Jingchao Wang, Lei Chen, Masoud Baikpour, Arinc Ozturk, Qian Li, Shinn-Huey Chou, et al. U-net using stacked dilated convolutions for medical image segmentation. arXiv preprint arXiv:2004.03466, 2020. STUDENT, PROF, COLLAB...

    2004 arXiv

  23. [31]

    Pvt v2: Improved baselines with pyramid vision transformer

    Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media, 8(3):415–424, 2022

    2022

  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 . In proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 , volume LNCS 15008. Springer Nature Switzerl...

    2024

  25. [33]

    An automatic nuclei image segmentation based on multi- scale split-attention u-net

    Qing Xu and Wenting Duan. An automatic nuclei image segmentation based on multi- scale split-attention u-net. In MICCAI Workshop on Computational Pathology, pages 236–245. PMLR, 2021

    2021

  26. [34]

    An effective cnn and transformer complementary network for medical image segmentation

    Feiniu Yuan, Zhengxiao Zhang, and Zhijun Fang. An effective cnn and transformer complementary network for medical image segmentation. Pattern Recognition, 136: 109228, 2023

    2023

  27. [35]

    Unet++: A nested u-net architecture for medical image segmentation

    Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLM...

    2018

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.