CP-UNet: Contour-based Probabilistic Model for Medical Ultrasound Images Segmentation

Jie Gao; Ruiguo Yu; Xuewei Li; Yiyang Zhang; Yuan Tian; Zhiqiang Liu

REVIEW 4 major objections 5 minor 22 references

CP-UNet: Contour-based Probabilistic Model for Medical Ultrasound Images Segmentation

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Contour-aware CP-UNet sharpens ultrasound lesion segmentation

desk verdict A plausible incremental architecture for contour-aware ultrasound segmentation, but the state-of-the-art claim is built on single-run differences within noise. read the letter →

arxiv 2411.14250 v1 pith:IN6OH4O2 submitted 2024-11-21 eess.IV cs.CV

Ruiguo Yu , Yiyang Zhang , Yuan Tian , Zhiqiang Liu , Xuewei Li , Jie Gao This is my paper · ORCID

classification eess.IVcs.CV

keywords medicalultrasoundsegmentationcontourprobabilisticmodelingGaussianmixturemodeldownsamplingbreastnodulethyroidU-Netboundaryuncertainty

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

The paper claims that explicitly modeling lesion boundaries as a probability distribution improves segmentation of blurred ultrasound contours. It proposes CP-UNet, which adds a contour probabilistic modeling module to a U-Net-style encoder-decoder, and reports higher Dice and IoU than strong convolutional, transformer, and attention baselines on three breast and thyroid ultrasound datasets. A sympathetic reader would care because blurred boundaries are a common obstacle in ultrasound-based diagnosis, and the method is designed to capture boundary uncertainty rather than ignore it.

What carries the argument

Three modules carry the argument. Multi-group Channel Shift Downsampling (MgCSD) splits features into channel groups, cyclically shifts most groups spatially, and re-fuses them with a global-pooling-weighted branch to give the encoder both local detail and global context. Contour Probabilistic Modeling (CPM) maps the downsampled features to K Gaussian mean-variance pairs, samples them via the reparameterization trick, and weights the samples with adaptive learnable parameters to approximate sampling from a Gaussian mixture. A gating-based feature filtering module (GF) then fuses upsampled, skip, and contour-sampled features at each decoding stage.

What would settle it

Run CP-UNet and the strongest baseline (pretrained TransUNet) at least five times each with different random seeds on the same BUSI, DDTI, and TUI splits, compute Dice and IoU confidence intervals, and check whether the intervals separate; if they overlap, the central claim does not hold.

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Extended reading notes

Core claim

CP-UNet guides the decoding stage with contour features sampled from a Gaussian mixture distribution fitted to the encoder's global-local features. The paper reports IoU/Dice of 0.6445/0.7827 on BUSI, 0.8136/0.8972 on DDTI, and 0.8496/0.9183 on TUI, with the largest margins over convolution baselines and results roughly comparable to a pretrained TransUNet while better following irregular contour zigzags. The authors state that their approach consistently improves segmentation accuracy for breast and thyroid nodules, outperforming strong baselines and state-of-the-art medical image segmentation methods.

Load-bearing premise

The reported metric differences come from a single training run per method, with no standard deviations or statistical tests, so the claim of consistent improvement assumes those small differences are not random training noise.

Editorial extensions

If this is right

If validated, CP-UNet offers a concrete way to reduce contour blurring in ultrasound segmentation tasks, improving boundary accuracy for lesion measurement and follow-up.
The probabilistic contour representation could provide not just a mask but a per-boundary uncertainty estimate, useful in clinical review workflows.
MgCSD is a drop-in replacement for standard downsampling and may improve other U-shaped segmentation networks on imaging modalities with noise artifacts.
The method's smaller-data gains on BUSI suggest the contour prior helps most when training data are limited.

Reading between the lines

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

The ablation results suggest most of the gain comes from MgCSD rather than the probabilistic contour modeling, since adding MgCSD alone raises IoU from 0.5439 to 0.6410 while CPM alone adds little; a tighter test would compare MgCSD-only variants against the full model on multiple seeds.
The claimed advantage over pretrained TransUNet is small in table metrics and rests on visual contour quality; a reader could test whether the reported boundary improvements translate to clinically meaningful contour errors.
A natural extension is to output the mixture variance as an uncertainty map for each predicted boundary and measure whether radiologists trust low-variance boundaries more under reading-time pressure.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Punchline: the architecture is a reasonable incremental contribution, but the empirical case for it is built on single-run differences of a few tenths of a percent, and the paper's own table shows CP-UNet does not beat pretrained TransUNet on IoU on two of three datasets. The right response is not panic, but: the method is plausible, the evaluation is not.

What's new: CP-UNet combines three modules: a channel-shift downsampling (MgCSD), a Gaussian-mixture contour modeling module with KL alignment to the mask's contour features, and a gating fusion module. The individual ideas are familiar—channel attention, spatial shift, reparameterized Gaussian sampling, gating—but the specific combination for contour-aware ultrasound segmentation is not something I've seen. The ablation is honest: MgCSD alone gives the big jump (~10 IoU points), CPM alone gives very little, and the full model is best. That pattern is internally consistent and suggests the authors understand their own modules.

The soft spots: the central claim 'outperforming state-of-the-art' is not supported by Table I. Against pretrained TransUNet, CP-UNet is lower in IoU on BUSI and DDTI, and the Dice gains are 0.0015 and 0.0009. On TUI the gains are 0.0015 IoU and 0.0017 Dice. These are within run-to-run noise for deep segmentation; without multiple seeds, error bars, or significance tests, the ordering is not established. The private TUI dataset is not described or released, so those numbers are unverifiable. The probabilistic contour model is also under-specified: the KL term is written for a single Gaussian but applied to K components, and the adaptive resampling weight Ω is described loosely. Minor issues: the Fig. 2 caption has a typo ('CMP'), and the paper says 'homoscedastic' in one place and 'homologous' in another.

Who it's for: someone working on ultrasound lesion segmentation who wants to try a contour-distribution prior. A serious referee should see it, mostly to push for proper statistical evaluation and a released/described private dataset. My own verdict would be reject as-is, but the idea is not dead; with multi-seed runs and error bars, or a more careful claim, it could be a decent workshop or short paper.

Referee Report

4 major / 5 minor

Summary. The paper proposes CP-UNet, a U-Net variant for medical ultrasound image segmentation. Three components are introduced: a multi-group channel-shift downsampling module (MgCSD) that creates global-local connections, a contour probabilistic modeling module (CPM) that uses a Gaussian mixture model to represent contour feature distributions and aligns them to ground-truth mask features via a KL divergence, and a gating-based feature filtering module (GF) that fuses contour features in the decoder. The model is trained with a combination of BCE, Dice, and KL losses. Experiments are reported on the public BUSI and DDTI datasets and a private TUI dataset, comparing against a range of U-Net, transformer, and attention-based baselines, with the claim that CP-UNet consistently outperforms state-of-the-art methods.

Significance. If the claimed improvements were properly supported, CP-UNet would be a useful contribution to ultrasound segmentation, specifically the idea of guiding contour representation through distributional alignment rather than a single contour loss. The paper retrains all baselines on the same datasets, which is good practice. However, the absence of error bars, the small and partially inconsistent differences with the pretrained TransUNet baseline, the uncharacterized private dataset, and the underspecified GMM formulation currently prevent the central claim from being assessed. The paper also provides no code, further limiting reproducibility.

major comments (4)

[§III-B, Table I] The central claim that CP-UNet 'consistently improves' segmentation and outperforms state-of-the-art methods is not supported by the reported numbers. On BUSI, CP-UNet's IoU is 0.6445 versus 0.6454 for pretrained TransUNet, and on DDTI the IoU is 0.8136 versus 0.8147; only on TUI is the IoU higher (0.8496 versus 0.8481). The Dice differences are all below 0.002. Because Table I reports only a single run per method and provides no standard deviations, confidence intervals, or significance tests, these differences are statistically indistinguishable from training variability. This is a load-bearing weakness for the paper's main claim, and the authors must provide multiple-seed results with error bars and a statistical comparison, or substantially revise the claim.
[§III-A, private TUI dataset] The TUI dataset, on which the largest relative improvements are reported, is described only as a private thyroid ultrasound dataset with 15,233 images. No information is provided about image acquisition, annotation protocol, patient demographics, inclusion/exclusion criteria, or the train/validation/test split, and the dataset is not released. This makes the TUI results impossible to verify or reproduce, and the reported gains may be specific to an uncharacterized data distribution. Please provide a detailed dataset description and release the data or at least the evaluation split.
[§II-B, Eq. (3)] The Gaussian mixture modeling is not implemented as a Gaussian mixture. Equation (3) samples a single z from N(0,1) and forms G = Ω(zσ_A + μ_A), which is a deterministic linear combination of Gaussian variables; a true mixture sample would first select a component index according to component weights and then sample from that component's Gaussian. The KL divergence in Eq. (4) is given for a single Gaussian pair, but the text says μ_A,σ_A and μ_B,σ_B contain K sets of Gaussians; no summation over K is specified. As a result, the training loss in Eq. (7) is underspecified, and the claim that the model 'samples from the mixed Gaussian distribution' is inaccurate. Please clarify the actual sampling and loss computation.
[§III-A, experimental protocol] The paper does not state how the datasets are split into training/validation/test sets, how many random seeds are used, or whether data augmentation and preprocessing are applied. It is also unclear whether the CPM sampling in Eq. (3) is performed at test time with a fixed z or averaged over multiple samples. The ablation study in Table II is based on a single run; the full model's improvement over MgCSD+CPM is only 0.0025 IoU (0.6445 versus 0.6420), which is within the range of random variation expected from the issues in Major Comment 1. These details are essential for any of the reported comparisons to be interpretable.

minor comments (5)

[Fig. 2 caption] The caption refers to 'CMP, Contour Probabilistic Modeling' but the module is consistently called CPM in the text; please unify the abbreviation.
[§II-A, Eq. (1)] The sentence '⊕ and ⊗ are element-wise operation' should say 'element-wise operations'.
[§III-C, Table II] The main text refers to 'Table III-C' but the table is labeled 'TABLE II'; correct the cross-reference.
[§II-B] The symbol L is used both for the number of encoder layers and for the globally enhanced feature F_L in Eq. (1); this is confusing and should be disambiguated.
[§II-B] The term 'adaptive homoscedastic resampling' is used for Eq. (3) but not defined; please explain what is adaptive and what is homoscedastic about the resampling.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning detected; the method's components are trained with standard supervised losses and the claims rest on empirical comparison, not on a self-referential derivation.

full rationale

The paper's derivation chain is self-contained in the relevant sense. CP-UNet's CPM module models contour features by sampling from a Gaussian mixture whose parameters are learned from the input image (Eq. 3), and the KL term (Eq. 4) aligns those learned parameters toward parameters extracted from the ground-truth mask during training. This is a conventional supervised/regularization setup: the network is trained to make its predicted contour distribution match the target mask distribution, and at inference only the image-derived distribution is used. The loss in Eq. 7 combines BCE, Dice, and KL, all of which are supervision signals; none of these equations reduce to a fitted constant or rename the target as a prediction. The MgCSD and GF modules are architectural contributions with no claimed derivation from first principles that would make them circular. There are no self-citations of the present authors invoked as load-bearing evidence, no imported uniqueness theorem, and no ansatz smuggled in via citation. The main weakness of the paper is empirical: the claim of outperforming state-of-the-art methods rests on single-run metric differences in Table I that are tiny and unreplicated (e.g., lower IoU than pretrained TransUNet on BUSI and DDTI, and Dice differences below 0.002). That is a correctness/reproducibility concern, not a circularity concern. Accordingly, the appropriate circularity score is 0.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim depends mainly on standard neural network training. The unstated hyperparameters are the number of Gaussian components K, the loss weighting, and the number of stages L. The domain assumptions are that contours can be modeled by a Gaussian mixture and that the mask-derived distribution is a valid target. No new physical entities are introduced.

free parameters (3)

Number of Gaussian components K
The CPM uses K Gaussian distributions per stage (Section II-B) but K is never specified.
KL divergence loss weight = 1 (implicit)
Eq. 7 sums BCE, Dice, and KL_AB with no weighting coefficients; this equal weighting is chosen by hand and not ablated.
Number of encoder stages L
L is used to denote the number of encoder layers feeding CPM but its value is not given.

assumptions (5)

standard math A Gaussian mixture distribution can approximate any continuous distribution arbitrarily well.
Invoked in Section II-B to justify using a mixture of Gaussians to model contour features.
standard math The reparameterization trick provides a differentiable way to sample from the modeled Gaussian distributions.
Used in Eq. 3 to sample z ~ N(0,1) and compute G = Omega(z*sigma_A + mu_A); cited to [16].
domain assumption Contour features in ultrasound images can be represented as samples from a Gaussian mixture distribution.
Core modeling hypothesis of CPM; the paper provides no empirical test that contours follow a mixture of Gaussians.
domain assumption The mask-processed image Y passed through the same feature extractor yields a valid reference distribution for the contour.
The KL objective in Eq. 4 aligns image-derived parameters to mask-derived parameters; the validity of this target is assumed, not validated.
ad hoc to paper The KL divergence between K pairs of Gaussians can be computed component-wise as in Eq. 4.
Eq. 4 is the standard KL for one Gaussian pair; the paper does not show the mixture KL with the learnable weights Omega.

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Cite this review

Pith. "Pith review of CP-UNet: Contour-based Probabilistic Model for Medical Ultrasound Images Segmentation." pith.science (2026). https://pith.science/paper/IN6OH4O2

@misc{pith2026241114250,
  author       = {Pith},
  title        = {Pith review of: CP-UNet: Contour-based Probabilistic Model for Medical Ultrasound Images Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IN6OH4O2}},
  note         = {Machine review of arXiv:2411.14250}
}

read the original abstract

Deep learning-based segmentation methods are widely utilized for detecting lesions in ultrasound images. Throughout the imaging procedure, the attenuation and scattering of ultrasound waves cause contour blurring and the formation of artifacts, limiting the clarity of the acquired ultrasound images. To overcome this challenge, we propose a contour-based probabilistic segmentation model CP-UNet, which guides the segmentation network to enhance its focus on contour during decoding. We design a novel down-sampling module to enable the contour probability distribution modeling and encoding stages to acquire global-local features. Furthermore, the Gaussian Mixture Model utilizes optimized features to model the contour distribution, capturing the uncertainty of lesion boundaries. Extensive experiments with several state-of-the-art deep learning segmentation methods on three ultrasound image datasets show that our method performs better on breast and thyroid lesions segmentation.

Figures

Figures reproduced from arXiv: 2411.14250 by the authors.

**Figure 2.** Framework of CP-UNet. CMP, Contour Probabilistic Modeling. MgCSD, Multi-group channel shifted downsampling. GF, Gating-based feature filtering [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 4.** Visualization of contour segmentation performance by TransUNet and [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

Figures from the paper (1 more)

**Figure 3.** Figure 3: Visualization of the results on two ultrasound images. The first three [PITH_FULL_IMAGE:figures/full_fig_p004_3.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

22 extracted references · 13 canonical work pages

[1]

U-net: Convolutional networks for biomedical image segmentation,

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international con- ference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241

2015
[2]

Unet++: A nested u-net architecture for medical image segmenta- tion,

Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmenta- tion,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Gra...

2018
[3]

Road extraction by deep residual u- net,

Z. Zhang, Q. Liu, and Y . Wang, “Road extraction by deep residual u- net,” IEEE Geoscience and Remote Sensing Letters , vol. 15, no. 5, pp. 749–753, 2018

work page 2018
[4]

Fully dense unet for 2-d sparse photoacoustic tomography artifact removal,

S. Guan, A. A. Khan, S. Sikdar, and P. V . Chitnis, “Fully dense unet for 2-d sparse photoacoustic tomography artifact removal,” IEEE Journal of Biomedical and Health Informatics , vol. 24, no. 2, pp. 568–576, 2020

work page 2020
[5]

Automatic thyroid ultra- sound image segmentation based on u-shaped network,

J. Ding, Z. Huang, M. Shi, and C. Ning, “Automatic thyroid ultra- sound image segmentation based on u-shaped network,” in 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) . IEEE, 2019, pp. 1–5

work page 2019
[6]

Unext: Mlp-based rapid medical image segmentation network,

J. M. J. Valanarasu and V . M. Patel, “Unext: Mlp-based rapid medical image segmentation network,” in International conference on medical image computing and computer-assisted intervention . Springer, 2022, pp. 23–33

2022
[7]

Contrans: Improving transformer with convolutional attention for medical image segmentation,

A. Lin, J. Xu, J. Li, and G. Lu, “Contrans: Improving transformer with convolutional attention for medical image segmentation,” in Med- ical Image Computing and Computer Assisted Intervention – MICCAI 2022, L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, and S. Li, Eds. Cham: Springer Nature Switzerland, 2022, pp. 297–307

work page 2022
[8]

Acc-unet: A completely convolutional unet model for the 2020s,

N. Ibtehaz and D. Kihara, “Acc-unet: A completely convolutional unet model for the 2020s,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2023, pp. 692–702

work page 2023

Show all 22 references

[9]

Segnetr: Rethinking the local-global interactions and skip connections in u-shaped networks,

J. Cheng, C. Gao, F. Wang, and M. Zhu, “Segnetr: Rethinking the local-global interactions and skip connections in u-shaped networks,” in International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 2023, pp. 64–74

2023
[10]

Global guidance network for breast lesion segmentation in ultrasound images,

C. Xue, L. Zhu, H. Fu, X. Hu, X. Li, H. Zhang, and P.-A. Heng, “Global guidance network for breast lesion segmentation in ultrasound images,” Medical image analysis , vol. 70, p. 101989, 2021

2021
[11]

Wavelet u-net++ for accurate lung nodule segmentation in ct scans: Improving early detection and diagnosis of lung cancer,

S. A. Agnes, A. A. Solomon, and K. Karthick, “Wavelet u-net++ for accurate lung nodule segmentation in ct scans: Improving early detection and diagnosis of lung cancer,” Biomedical Signal Processing and Control, vol. 87, p. 105509, 2024

2024
[12]

Learning active contour models based on self-attention for breast ultrasound image segmentation,

Y . Zhao, X. Shen, J. Chen, W. Qian, L. Sang, and H. Ma, “Learning active contour models based on self-attention for breast ultrasound image segmentation,” Biomedical Signal Processing and Control , vol. 89, p. 105816, 2024

2024
[13]

Dataset of breast ultrasound images,

W. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of breast ultrasound images,” Data in Brief , vol. 28, p. 104863, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S2352340919312181

2020
[14]

An open access thyroid ultrasound image database,

L. Pedraza, C. Vargas, F. Narv ´aez, O. Dur ´an, E. Mu ˜noz, and E. Romero, “An open access thyroid ultrasound image database,” in 10th International Symposium on Medical Information Processing and Analysis, E. Romero and N. Lepore, Eds., vol. 9287, International Society for O...

2015 doi
[15]

Aau-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images,

G. Chen, L. Li, Y . Dai, J. Zhang, and M. H. Yap, “Aau-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images,” IEEE Transactions on Medical Imaging , vol. 42, no. 5, pp. 1289–1300, 2022

2022
[16]

Auto-encoding variational bayes,

D. P. Kingma, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013

2013 arXiv
[17]

Gart: Gaussian articulated template models,

J. Lei, Y . Wang, G. Pavlakos, L. Liu, and K. Daniilidis, “Gart: Gaussian articulated template models,” in Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 19 876–19 887

2024
[18]

Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,

S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y . Wang, Y . Fu, J. Feng, T. Xi- ang, P. H. Torr, and L. Zhang, “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec...

2021
[19]

Transunet: Transformers make strong encoders for medical image segmentation,

J. Chen, Y . Lu, Q. Yu, X. Luo, E. Adeli, Y . Wang, L. Lu, A. L. Yuille, and Y . Zhou, “Transunet: Transformers make strong encoders for medical image segmentation,” arXiv preprint arXiv:2102.04306 , 2021

2021 arXiv
[20]

Unetr: Transformers for 3d medical image segmentation,

A. Hatamizadeh, Y . Tang, V . Nath, D. Yang, A. Myronenko, B. Landman, H. R. Roth, and D. Xu, “Unetr: Transformers for 3d medical image segmentation,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2022, pp. 574–584

2022
[21]

Medical transformer: Gated axial-attention for medical image segmentation,

J. M. J. Valanarasu, P. Oza, I. Hacihaliloglu, and V . M. Patel, “Medical transformer: Gated axial-attention for medical image segmentation,” in Medical image computing and computer assisted intervention–MICCAI 2021: 24th international conference, Strasbourg, France, September...

2021
[22]

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

H. Cao, Y . Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmenta- tion,” in European conference on computer vision . Springer, 2022, pp. 205–218

2022

Pith tools

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

[1] [1]

U-net: Convolutional networks for biomedical image segmentation,

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international con- ference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241

2015

[2] [2]

Unet++: A nested u-net architecture for medical image segmenta- tion,

Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++: A nested u-net architecture for medical image segmenta- tion,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Gra...

2018

[3] [3]

Road extraction by deep residual u- net,

Z. Zhang, Q. Liu, and Y . Wang, “Road extraction by deep residual u- net,” IEEE Geoscience and Remote Sensing Letters , vol. 15, no. 5, pp. 749–753, 2018

work page 2018

[4] [4]

Fully dense unet for 2-d sparse photoacoustic tomography artifact removal,

S. Guan, A. A. Khan, S. Sikdar, and P. V . Chitnis, “Fully dense unet for 2-d sparse photoacoustic tomography artifact removal,” IEEE Journal of Biomedical and Health Informatics , vol. 24, no. 2, pp. 568–576, 2020

work page 2020

[5] [5]

Automatic thyroid ultra- sound image segmentation based on u-shaped network,

J. Ding, Z. Huang, M. Shi, and C. Ning, “Automatic thyroid ultra- sound image segmentation based on u-shaped network,” in 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) . IEEE, 2019, pp. 1–5

work page 2019

[6] [6]

Unext: Mlp-based rapid medical image segmentation network,

J. M. J. Valanarasu and V . M. Patel, “Unext: Mlp-based rapid medical image segmentation network,” in International conference on medical image computing and computer-assisted intervention . Springer, 2022, pp. 23–33

2022

[7] [7]

Contrans: Improving transformer with convolutional attention for medical image segmentation,

A. Lin, J. Xu, J. Li, and G. Lu, “Contrans: Improving transformer with convolutional attention for medical image segmentation,” in Med- ical Image Computing and Computer Assisted Intervention – MICCAI 2022, L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, and S. Li, Eds. Cham: Springer Nature Switzerland, 2022, pp. 297–307

work page 2022

[8] [8]

Acc-unet: A completely convolutional unet model for the 2020s,

N. Ibtehaz and D. Kihara, “Acc-unet: A completely convolutional unet model for the 2020s,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2023, pp. 692–702

work page 2023