Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

Changming Sun; Cong Cong; Hui Cui; Junbo Wang; Leyi Wei; Linlin Wang; Ping Xuan; Qiangguo Jin; Ran Su; Yimiao He

REVIEW 3 major objections 3 minor 48 references

Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation

T0 review · 3 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that steering copy-paste augmentation by model uncertainty and refining pseudo-labels over iterations makes semi-supervised tumor segmentation beat existing SSL baselines on CT scans.

desk verdict Plausible SSL pipeline for small/multiple tumor segmentation, but our copy's full text is unreadable, so the central claims are unverifiable from what I have. read the letter →

arxiv 2508.04044 v1 pith:ZTB4P4GI submitted 2025-08-06 cs.CV

Qiangguo Jin , Hui Cui , Junbo Wang , Changming Sun , Yimiao He , Ping Xuan , Linlin Wang , Cong Cong

show 2 more authors

Leyi Wei Ran Su

This is my paper · ORCID

classification cs.CV

keywords semi-supervisedlearningtumorsegmentationCTscanspseudo-labelingcopy-pasteaugmentationuncertaintyestimationmeanteachermedicalimage

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is trying to establish that a small, targeted change to semi-supervised training—using the model's own uncertainty to decide where to apply copy-paste augmentation, and revising pseudo-labels over iterations—can improve tumor segmentation when only a fraction of CT scans have labels. The authors position this against the common SSL setup that segments large organs and struggles with the harder case of many small tumors. On an in-house and a public CT dataset, they report that their IPA-CP framework outperforms state-of-the-art semi-supervised methods, and ablations attribute the gains to the two proposed mechanisms. If the claim holds, unlabeled CT archives become usable for training small-lesion segmenters with far less manual annotation.

What carries the argument

The load-bearing machinery is the two-way uncertainty based adaptive augmentation: uncertainty estimates produced by the mean teacher are used to modulate copy-paste exchanges between labeled and unlabeled data, focusing augmentation on regions where the model is unsure. Paired with it is the iterative pseudo-label transition strategy, which progressively refines the pseudo-labels used for unlabeled samples rather than treating a single static prediction as ground truth. The work these two mechanisms do is to convert the augmentation pipeline and the pseudo-label loop into error-aware supervision, which is what the ablation studies isolate.

What would settle it

Compute per-voxel uncertainty from the mean teacher and compare it to the student's actual segmentation errors on a labeled holdout set. If high-uncertainty regions do not contain a disproportionate share of errors—or if replacing the uncertainty-guided copy-paste with randomly selected copy-paste preserves the reported gains—the central mechanism is not doing the work claimed. A second decisive check is to rerun training with the iterative pseudo-label transition disabled while keeping everything else fixed; if performance does not drop, the transition strategy is not load-bearing.

Watch

Extended reading notes

Core claim

The central claim is that a mean-teacher semi-supervised segmentation network can be made markedly more accurate on tumor CT scans by coupling two mechanisms: an uncertainty-guided adaptive copy-paste augmentation, which injects tumor-related uncertainty from the teacher into how labeled and unlabeled patches are pasted together, and an iterative pseudo-label transition strategy, which updates pseudo-labels across training rounds instead of fixing them after a single pass. The authors contend that the mean teacher's uncertainty is a usable signal for where pseudo-labels are unreliable, so concentrating augmentation at those locations turns augmentation itself into a supervisory cue. They rep

Load-bearing premise

The method depends on the mean teacher's uncertainty maps being a trustworthy proxy for where pseudo-labels are wrong, so that concentrating copy-paste there improves learning instead of amplifying noise.

Editorial extensions

If this is right

If the reported gains hold, semi-supervised tumor segmentation becomes practical in the many-small-tumor regime, not just for large organs.
Uncertainty from a mean teacher can be used as a spatial guide for data augmentation, not only for sample selection or confidence thresholds.
Iteratively transitioning pseudo-labels adds a cheap refinement loop that could be layered onto existing consistency-regularization methods.
The framework reduces the dependence on large labeled CT datasets by leveraging unlabeled scans to teach small-tumor appearance.
Copy-paste supervision makes augmentation a supervisory signal in its own right for dense prediction tasks.

Reading between the lines

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

The underlying principle—spend augmentation effort where the model is most uncertain—should transfer to other medical imaging tasks such as MRI, ultrasound, or histopathology if the uncertainty estimates stay calibrated; the paper only demonstrates CT tumors.
The copy-paste design implicitly assumes tumor patches are roughly appearance-only objects that can be transplanted across images; for lesions whose appearance depends on location or organ context, the gains may shrink—a boundary the paper does not test.
One could isolate the contribution of the uncertainty guidance by benchmarking IPA-CP against the same copy-paste schedule with random location selection; that ablation would separate 'copy-paste helps' from 'uncertainty-guided copy-paste helps.'
The iterative pseudo-label transition resembles a curriculum over label trust; combining it with strict confidence thresholds or student-teacher disagreement filters could either compound or conflict with the reported gains.

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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

Here's my honest read. I could not actually read this paper: the full text in our copy is garbled to the point of being indecipherable, so this assessment is based on the abstract and the visible structure. If the actual PDF is intact, what follows is a provisional take.

The paper takes on a real gap: semi-supervised segmentation has mostly focused on large organs, not small or multiple tumors in CT. That is a practical problem worth solving. The proposed IPA-CP is a sensible combination of known pieces—mean teacher, pseudo-labeling, uncertainty estimation, copy-paste augmentation—plus an iterative pseudo-label transition to manage the self-training loop. The abstract's phrase "straightforward yet effective" is honest about the level of novelty. The paper claims experiments on both in-house and public datasets with ablations, which is good practice.

The soft spots are exactly what I cannot check from the corrupted text. The central mechanism is uncertainty-guided copy-paste: the authors inject mean-teacher uncertainty into augmentation so that copy-paste is directed at likely-wrong regions. That assumes the uncertainty estimates are a reliable proxy for pseudo-label error, which is not guaranteed for small, low-contrast lesions. The stress-test concern is fair: without a control that swaps adaptive selection for random selection at matched augmentation intensity, the gains could come from augmentation volume or the iterative schedule rather than the uncertainty selection. I cannot tell whether that control exists because the experiments are unreadable here. Similarly, the iterative pseudo-label transition is a standard way to mitigate confirmation bias, but its contribution needs to be isolated; the abstract only promises an ablation.

Who is this for? A specialist in medical image segmentation or semi-supervised learning who wants to know whether directing copy-paste by uncertainty helps small tumors. If the full text is intact and the missing control is in there, this could be a useful incremental contribution. If not, the headline claim is under-supported.

My recommendation: a serious editor should send this to peer review rather than desk reject it—the problem is relevant and the method is sensible. But the reviewers need a clean copy and should specifically check whether the ablation includes a random-selection control at matched augmentation intensity. For my own work, I would not cite it until I can verify the results.

Referee Report

3 major / 3 minor

Summary. The manuscript proposes IPA-CP, an iterative pseudo-labeling framework with adaptive copy-paste supervision for semi-supervised tumor segmentation in CT. The core ideas are (i) a 'two-way uncertainty based adaptive augmentation' mechanism that transfers uncertainty estimates from a mean-teacher architecture into copy-paste augmentation, and (ii) an iterative pseudo-label transition strategy to generate pseudo labels for unlabeled data. The abstract claims that extensive experiments on in-house and public datasets show state-of-the-art performance. The provided full text, however, is severely corrupted: most paragraphs, equations, and tables are rendered as replacement/block characters, so the experimental design, baselines, ablations, and numerical results behind the central claim cannot be inspected.

Significance. If the empirical claim held, the contribution would be relevant to SSL for medical image segmentation, since small tumors are a recognized challenge and augmentation for unlabeled data is underexplored. The idea of steering copy-paste with mean-teacher uncertainty is also plausible and worth testing. However, the presented manuscript does not provide legible evidence. There are no machine-checked proofs, reproducible code, or parameter-free derivations visible, and the contribution is empirical; its significance therefore rests entirely on experiments and ablations that are unreadable in this copy. The paper's potential significance cannot be assessed on the supplied text.

major comments (3)

[Full text (all sections)] The submitted text is unreadable due to character corruption. The experimental section, tables, and equations appear as repeated block characters, and no numeric result, baseline, error bar, or dataset statistic is legible. Since the central claim in the Abstract ('outperforms state-of-the-art SSL methods') is purely empirical, this corruption is load-bearing: I cannot verify the existence or validity of the experiments, and I cannot evaluate the soundness of the reported gains.
[Abstract] The claimed benefit of 'two-way uncertainty based adaptive augmentation' is not supported by any visible isolation of the uncertainty-selection component. The method also includes iterative pseudo-label transition and copy-paste augmentation volume, so the abstract's attribution of the gains to uncertainty-guided selection requires an ablation that swaps uncertainty-guided selection for random selection at matched augmentation intensity and matched pseudo-label schedule. The available text provides no such control, and the corrupted tables prevent verification.
[Page 1 header] The running header cites 'arXiv:2508.04045v2 [cs.LG] 15 Mar 2026', which conflicts with the manuscript ID 2508.04044 and the stated cs.CV category. This suggests the submitted source files were not properly assembled, and it makes it impossible to know whether the corrupted text is the intended version of the manuscript.

minor comments (3)

[Abstract] The term 'two-way uncertainty' is not defined. Please specify whether it refers to predictive entropy, variance, teacher-student disagreement, or another estimator, so that the mechanism can be reproduced.
[§3 (methods)] In the partially readable fragments, several symbols appear without visible definitions. Please ensure all notation in the final version is defined at first use.
[Tables and figures] All table cells and figure captions are corrupted in the provided copy; the final PDF should embed fonts and use standard encoding so that numerical results are legible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detectable from the available text; the paper's empirical SSL claim is not a derivation that reduces to its own inputs.

full rationale

The provided full text is heavily corrupted, so the only reliably readable material is the abstract and a few fragments. The abstract's central claim is empirical: 'Extensive experiments on both in-house and public datasets show that our framework outperforms state-of-the-art SSL methods in medical image segmentation.' This is an externally falsifiable experimental claim, not a quantity derived from the method's own definitions or fitted parameters. The method uses a mean-teacher architecture, uncertainty estimates, pseudo-labeling, and copy-paste augmentation; these are standard SSL training mechanisms. Even though pseudo-labeling is a self-training loop that can carry confirmation-bias risk, that is a performance/validation concern, not circularity in the sense of the rules here: there is no quoted equation showing that a predicted quantity is identical by construction to an input, and no load-bearing self-citation chain can be inspected in the corrupted text. The skeptical concern about uncertainty miscalibration is a correctness risk, not a demonstrated circular step. Accordingly, no specific circular step quotient can be exhibited, and the appropriate honest finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

Only the abstract was readable; the full text as supplied is corrupted to the point of being indecipherable. We therefore cannot identify free parameters, additional axioms, or invented entities beyond the abstract's domain assumptions.

assumptions (2)

domain assumption Semi-supervised learning with consistency regularization and pseudo-labeling improves tumor segmentation with limited labeled data.
The method is built on this SSL premise; abstract states SSL has attracted considerable attention and the paper proposes improvements.
domain assumption The mean teacher's uncertainty estimates reflect segmentation errors in tumor regions.
The adaptive augmentation mechanism injects 'tumor uncertainties present in the mean teacher architecture' (abstract). If this fails, the augmentation may not help.

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Cite this review

Pith. "Pith review of Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation." pith.science (2026). https://pith.science/paper/ZTB4P4GI

@misc{pith2026250804044,
  author       = {Pith},
  title        = {Pith review of: Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZTB4P4GI}},
  note         = {Machine review of arXiv:2508.04044}
}

read the original abstract

Semi-supervised learning (SSL) has attracted considerable attention in medical image processing. The latest SSL methods use a combination of consistency regularization and pseudo-labeling to achieve remarkable success. However, most existing SSL studies focus on segmenting large organs, neglecting the challenging scenarios where there are numerous tumors or tumors of small volume. Furthermore, the extensive capabilities of data augmentation strategies, particularly in the context of both labeled and unlabeled data, have yet to be thoroughly investigated. To tackle these challenges, we introduce a straightforward yet effective approach, termed iterative pseudo-labeling based adaptive copy-paste supervision (IPA-CP), for tumor segmentation in CT scans. IPA-CP incorporates a two-way uncertainty based adaptive augmentation mechanism, aiming to inject tumor uncertainties present in the mean teacher architecture into adaptive augmentation. Additionally, IPA-CP employs an iterative pseudo-label transition strategy to generate more robust and informative pseudo labels for the unlabeled samples. Extensive experiments on both in-house and public datasets show that our framework outperforms state-of-the-art SSL methods in medical image segmentation. Ablation study results demonstrate the effectiveness of our technical contributions.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

48 extracted references · 43 canonical work pages

[1]

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 ":" * " " * FUNCTION f...
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" 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 ":" * " " * FUNCTION f...
[3]

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 ":" * " " * FUNCTION f...
[4]

X. Li, L. Chen, S. Luan, J. Zhou, X. Xiao, Y. Yang, C. Mao, P. Fang, L. Chen, X. Zeng, et al., The development and progress of nanomedicine for esophageal cancer diagnosis and treatment, in: Seminars in Cancer Biology, Elsevier, 2022

work page 2022
[5]

X. Ye, D. Guo, C.-K. Tseng, J. Ge, T.-M. Hung, P.-C. Pai, Y. Ren, L. Zheng, X. Zhu, L. Peng, et al., Multi-institutional validation of two-streamed deep learning method for automated delineation of esophageal gross tumor volume using planning CT and FDG-PET/CT , Frontiers in Oncology 11 (2022) 5809

work page 2022
[6]

Gruber, S

N. Gruber, S. Antholzer, W. Jaschke, C. Kremser, M. Haltmeier, A joint deep learning approach for automated liver and tumor segmentation, in: 2019 13th International Conference on Sampling Theory and Applications (SampTA), IEEE, 2019, pp. 1--5

work page 2019
[7]

Patel, D

D. Patel, D. Patel, R. Saxena, T. Akilan, Multi-class Brain Tumor Segmentation using Graph Attention Network , arXiv preprint arXiv:2302.05598 (2023)

arXiv 2023
[8]

W. Bai, O. Oktay, M. Sinclair, H. Suzuki, M. Rajchl, G. Tarroni, B. Glocker, A. King, P. M. Matthews, D. Rueckert, Semi-supervised learning for network-based cardiac MR image segmentation , in: Medical Image Computing and Computer-Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings...

work page 2017

Show all 48 references

[9]

Ouali, C

Y. Ouali, C. Hudelot, M. Tami, Semi-supervised semantic segmentation with cross-consistency training, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 12674--12684

2020
[10]

X. Chen, Y. Yuan, G. Zeng, J. Wang, Semi-supervised semantic segmentation with cross pseudo supervision, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 2613--2622

2021
[11]

T. Lei, D. Zhang, X. Du, X. Wang, Y. Wan, A. K. Nandi, Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network, IEEE Transactions on Medical Imaging 42 (5) (2023) 1265--1277. https://doi.org/10.1109/TMI.2022.3225687 doi...

2023
[12]

L. Yu, S. Wang, X. Li, C.-W. Fu, P.-A. Heng, Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation , in: International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, 2019, pp. 605--613

2019
[13]

Y. Bai, D. Chen, Q. Li, W. Shen, Y. Wang, Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11514--11524

2023
[14]

X. Yu, Q. Ma, T. Ling, J. Zhu, Y. Shi, Enhancing semi-supervised medical image segmentation with bidirectional copy-paste and masked image reconstruction, International Journal of Machine Learning and Cybernetics (2024) 1--11

2024
[15]

B. Song, Q. Wang, Sdcl: Students discrepancy-informed correction learning for semi-supervised medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2024, pp. 567--577

2024
[16]

J. Wang, D. Ruan, Y. Li, Z. Wang, Y. Wu, T. Tan, G. Yang, M. Jiang, Data augmentation strategies for semi-supervised medical image segmentation, Pattern Recognition 159 (2025) 111116

2025
[17]

Y. Wang, B. Xiao, X. Bi, W. Li, X. Gao, MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 15651--15660

2023
[18]

X. Luo, G. Wang, W. Liao, J. Chen, T. Song, Y. Chen, S. Zhang, D. N. Metaxas, S. Zhang, Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency, Medical Image Analysis 80 (2022) 102517

2022
[19]

Y. Wu, M. Xu, Z. Ge, J. Cai, L. Zhang, Semi-supervised left atrium segmentation with mutual consistency training, in: Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proc...

2021
[20]

Y. Wu, Z. Ge, D. Zhang, M. Xu, L. Zhang, Y. Xia, J. Cai, Mutual consistency learning for semi-supervised medical image segmentation, Medical Image Analysis 81 (2022) 102530

2022
[21]

L. Wang, J. Wang, L. Zhu, H. Fu, P. Li, G. Cheng, Z. Feng, S. Li, P.-A. Heng, Dual Multiscale Mean Teacher Network for Semi-Supervised Infection Segmentation in Chest CT Volume for COVID-19 , IEEE Transactions on Cybernetics 53 (10) (2023) 6363--6375. https://doi.org/10.1109/T...

2023
[22]

Zhang, C

Z. Zhang, C. Tian, X. Gao, C. Wang, X. Feng, H. X. Bai, Z. Jiao, Dynamic prototypical feature representation learning framework for semi-supervised skin lesion segmentation, Neurocomputing 507 (2022) 369--382

2022
[23]

Basak, Z

H. Basak, Z. Yin, Pseudo-label guided contrastive learning for semi-supervised medical image segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 19786--19797

2023
[24]

J. Su, Z. Luo, S. Lian, D. Lin, S. Li, Mutual learning with reliable pseudo label for semi-supervised medical image segmentation, Medical Image Analysis 94 (2024) 103111

2024
[25]

Ranjbarzadeh, A

R. Ranjbarzadeh, A. Bagherian Kasgari, S. Jafarzadeh Ghoushchi, S. Anari, M. Naseri, M. Bendechache, Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images , Scientific Reports 11 (1) (2021) 10930

2021
[26]

Jiang, Z

H. Jiang, Z. Diao, Y.-D. Yao, Deep learning techniques for tumor segmentation: a review, The Journal of Supercomputing 78 (2) (2022) 1807--1851

2022
[27]

Ronneberger, P

O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional networks for biomedical image segmentation , in: International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, 2015, pp. 234--241

2015
[28]

P. Wang, A. C. Chung, Relax and focus on brain tumor segmentation, Medical Image Analysis 75 (2022) 102259

2022
[29]

X. Yu, Q. Yang, Y. Zhou, L. Y. Cai, R. Gao, H. H. Lee, T. Li, S. Bao, Z. Xu, T. A. Lasko, et al., UNesT: local spatial representation learning with hierarchical transformer for efficient medical segmentation , Medical Image Analysis 90 (2023) 102939

2023
[30]

Zhang, X

X. Zhang, X. Zhu, K. Tang, Y. Zhao, Z. Lu, Q. Feng, DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer , Medical Image Analysis 78 (2022) 102415

2022
[31]

P. F. Christ, F. Ettlinger, F. Gr \"u n, M. E. A. Elshaera, J. Lipkova, S. Schlecht, F. Ahmaddy, S. Tatavarty, M. Bickel, P. Bilic, et al., Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks , arXiv preprint arXiv:17...

2017 arXiv
[32]

H.-Y. Zhou, J. Guo, Y. Zhang, X. Han, L. Yu, L. Wang, Y. Yu, nnFormer: volumetric medical image segmentation via a 3D transformer , IEEE Transactions on Image Processing 32 (2023) 4036--4045. https://doi.org/10.1109/TIP.2023.3293771 doi:10.1109/TIP.2023.3293771

2023
[33]

X. Luo, J. Chen, T. Song, G. Wang, Semi-supervised medical image segmentation through dual-task consistency, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 8801--8809

2021
[34]

Z. Cai, J. Xin, T. Zeng, S. Dong, N. Zheng, J. S. Duncan, Class-Aware Mutual Mixup with Triple Alignments for Semi-supervised Cross-Domain Segmentation , in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2024, pp. 68--79

2024
[35]

J. Miao, C. Chen, K. Zhang, J. Chuai, Q. Li, P.-A. Heng, Cross prompting consistency with segment anything model for semi-supervised medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2024, pp. 167--177

2024
[36]

Chaitanya, E

K. Chaitanya, E. Erdil, N. Karani, E. Konukoglu, Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation, Medical Image Analysis 87 (2023) 102792

2023
[37]

S. K. Hasan, C. Linte, STAMP: A Self-training Student-Teacher Augmentation-Driven Meta Pseudo-Labeling Framework for 3D Cardiac MRI Image Segmentation , in: Annual Conference on Medical Image Understanding and Analysis, Springer, 2022, pp. 371--386

2022
[38]

K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, C.-L. Li, FixMatch: Simplifying semi-supervised learning with consistency and confidence , Advances in Neural Information Processing Systems 33 (2020) 596--608

2020
[39]

Y. Chen, X. Tan, B. Zhao, Z. Chen, R. Song, J. Liang, X. Lu, Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 7548--7557

2023
[40]

T. Wei, K. Gan, Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency Is All You Need , in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 3469--3478

2023
[41]

Zheng, Y

Z. Zheng, Y. Yang, Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation, International Journal of Computer Vision 129 (4) (2021) 1106--1120

2021
[42]

F. Lyu, M. Ye, J. F. Carlsen, K. Erleben, S. Darkner, P. C. Yuen, Pseudo-Label Guided Image Synthesis for Semi-Supervised COVID-19 Pneumonia Infection Segmentation , IEEE Transactions on Medical Imaging 42 (3) (2022) 797--809

2022
[43]

Q. Jin, H. Cui, C. Sun, Y. Song, J. Zheng, L. Cao, L. Wei, R. Su, Inter-and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation, Expert Systems with Applications 238 (2024) 122093

2024
[44]

Antonelli, A

M. Antonelli, A. Reinke, S. Bakas, K. Farahani, A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, O. Ronneberger, R. M. Summers, et al., The Medical Segmentation Decathlon , Nature Communications 13 (1) (2022) 4128

2022
[45]

Tarvainen, H

A. Tarvainen, H. Valpola, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results , in: Advances in Neural Information Processing Systems, 2017, pp. 1195--1204

2017
[46]

Verma, A

V. Verma, A. Lamb, J. Kannala, Y. Bengio, D. Lopez-Paz, Interpolation Consistency Training for Semi-supervised Learning , in: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI'19, AAAI Press, 2019, pp. 3635--3641

2019
[47]

L. Wu, J. Li, Y. Wang, Q. Meng, T. Qin, W. Chen, M. Zhang, T.-Y. Liu, et al., R-Drop: Regularized dropout for neural networks , Advances in Neural Information Processing Systems 34 (2021) 10890--10905

2021
[48]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

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 ":" * " " * FUNCTION f...

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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 ":" * " " * FUNCTION f...

[4] [4]

X. Li, L. Chen, S. Luan, J. Zhou, X. Xiao, Y. Yang, C. Mao, P. Fang, L. Chen, X. Zeng, et al., The development and progress of nanomedicine for esophageal cancer diagnosis and treatment, in: Seminars in Cancer Biology, Elsevier, 2022

work page 2022

[5] [5]

X. Ye, D. Guo, C.-K. Tseng, J. Ge, T.-M. Hung, P.-C. Pai, Y. Ren, L. Zheng, X. Zhu, L. Peng, et al., Multi-institutional validation of two-streamed deep learning method for automated delineation of esophageal gross tumor volume using planning CT and FDG-PET/CT , Frontiers in Oncology 11 (2022) 5809

work page 2022

[6] [6]

Gruber, S

N. Gruber, S. Antholzer, W. Jaschke, C. Kremser, M. Haltmeier, A joint deep learning approach for automated liver and tumor segmentation, in: 2019 13th International Conference on Sampling Theory and Applications (SampTA), IEEE, 2019, pp. 1--5

work page 2019

[7] [7]

Patel, D

D. Patel, D. Patel, R. Saxena, T. Akilan, Multi-class Brain Tumor Segmentation using Graph Attention Network , arXiv preprint arXiv:2302.05598 (2023)

arXiv 2023

[8] [8]

W. Bai, O. Oktay, M. Sinclair, H. Suzuki, M. Rajchl, G. Tarroni, B. Glocker, A. King, P. M. Matthews, D. Rueckert, Semi-supervised learning for network-based cardiac MR image segmentation , in: Medical Image Computing and Computer-Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings...

work page 2017