PDC-Net: Pattern Divide-and-Conquer Network for Pelvic Radiation Injury Segmentation

Chong Gao; Guanbin Li; Lei Zhang; Qiyuan Qin; Wuteng Cao; Xinyu Xiong; Zihuang Wu

REVIEW 4 major objections 5 minor 18 references

PDC-Net: Pattern Divide-and-Conquer Network for Pelvic Radiation Injury Segmentation

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read A pattern divide-and-conquer network for pelvic radiation injury segmentation achieves the best reported Dice and MCC on a 344-case MRI dataset, with every module contributing a measurable gain.

desk verdict A genuinely new segmentation task and dataset, with a coherent architecture and an unproven SOTA claim due to single-run evaluations. read the letter →

arxiv 2506.17712 v1 pith:OIX7NZQ5 submitted 2025-06-21 cs.CV

Xinyu Xiong , Wuteng Cao , Zihuang Wu , Lei Zhang , Chong Gao , Guanbin Li , Qiyuan Qin This is my paper · ORCID

classification cs.CV

keywords pelvicradiationinjuryMRIsegmentationdeeplearningimagestripconvolutionmemory-guidedcontextmixture-of-expertsmedicalanalysis

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

Segmentation of pelvic radiation injury (PRI) from MRI is the kind of task that decides whether radiotherapy follow-up can be quantified: the injured structures are hollow organs and thin muscles that appear as circular or strip-like shapes, and injured and uninjured regions can sit in the same slice. The paper's central claim is that a pattern divide-and-conquer network, PDC-Net, fits these shapes and resolves the confusing context by assigning three modules to three pattern classes—directional strip convolutions for local shape, a dataset-level memory bank for global context, and a mixture-of-experts decoder for adaptive fusion. Evaluated on the first large-scale PRI dataset (344 pelvic cases, 8,049 axial T2-weighted MRI images), PDC-Net reports the best Dice and Matthews correlation among six comparison methods, with positive-class DSC 49.12 versus 46.30 for the strongest baseline EMCAD. The ablations show each module adding a measurable gain over its direct alternative, consistent with the divide-and-conquer rationale.

What carries the argument

The load-bearing machinery is the pattern divide-and-conquer composition: three modules, each responsible for one failure mode, assembled so the decoder can route among them. The Multi-Direction Aggregation (MDA) module is a bank of four strip convolutions in directions $[1,0]$, $[0,1]$, $[1,1]$, and $[-1,1]$ with kernel size 9, whose outputs are concatenated and element-wise multiplied by a max-pooled detail branch; it exists because square convolutions capture circular and strip-like organ shapes inefficiently. The Memory-Guided Context (MGC) module maintains a dataset-level memory bank $\mathcal{M}\in\mathbb{R}^{1\times1\times S\times K}$; pooled feature patches query it through softmax similarity weights, so the network accumulates and retrieves foreground-specific patterns across images and can suppress background. The Adaptive Fusion Decoder (AFD) is a mixture-of-experts decoder: features from four stages are patch-shuffled, passed through four depthwise-convolution experts with $1\times n$ and $n\times1$ kernels for $n\in\{3,5,7,9\}$, and fused by sigmoid-gated per-expert weights. Together they implement the paper's claim that local shape, global context, and fusion should be handled by dedicated, separable mechanisms.

What would settle it

Run PDC-Net, EMCAD, and the ablation variants under at least five random seeds on the same 274-case training split and the fixed 70-case test split; if the 95% confidence interval for the positive-class DSC difference between PDC-Net and EMCAD includes zero, the reported state-of-the-art claim is not supported.

Watch

Extended reading notes

Core claim

PDC-Net is built on a PVT-v2-b2 encoder followed by three components. The Multi-Direction Aggregation (MDA) module splits the encoder features into four channel groups and applies strip convolutions in horizontal, vertical, left-diagonal, and right-diagonal directions with kernel size 9, then multiplies the concatenated result by a max-pooling detail branch, letting the network fit the circular and elongated shapes of the rectal wall, anal sphincter, puborectalis muscle, and bladder. The Memory-Guided Context (MGC) module slices the deep feature map into patches, pools each patch, and queries a memory bank $\mathcal{M}\in\mathbb{R}^{1\times1\times S\times K}$ with softmax-computed similarity weights, reinforcing foreground-related patterns and suppressing background. The Adaptive Fusion Decoder (AFD) mixes features from four stages through patch shuffle, applies four depthwise-convolution experts with $n\times1$ and $1\times n$ kernels for $n\in\{3,5,7,9\}$, and combines expert predictions with sigmoid-gated weights. On the in-house dataset of 344 pelvic cases (8,049 T2-weighted axial images), the paper reports negative-class DSC 48.79 and positive-class DSC 49.12, compared with 46.27 and 46.30 for EMCAD, and shows in ablations that MDA, MGC, and AFD each outperform their direct replacements.

Load-bearing premise

The claim that PDC-Net is superior rests on the assumption that its 2-3 percentage-point Dice advantages over the strongest baseline are real differences rather than run-to-run noise, because the paper reports a single run per method without error bars or significance tests.

Editorial extensions

If this is right

Pelvic radiation injury segmentation now has a concrete automated baseline: 344 cases and 8,049 images, with negative/positive DSC of 48.79/49.12 and MCC of 50.31/50.98, against which later methods can be compared.
Four-direction strip convolutions, including diagonals, improve over two-direction strip convolutions, so thin and diagonal anatomical structures are better served by directional kernels than by square kernels alone.
A dataset-level memory bank queried by pooled patches can separate positive from negative class patterns without dilated convolutions or self-attention, under the class imbalance this dataset exhibits.
An MoE decoder that mixes features from multiple stages by patch shuffle and expert-specific depthwise convolutions beats both a U-shaped decoder and a vanilla MoE decoder.
PDC-Net is presented as the first AI method for PRI segmentation, so this paper defines the task's evaluation setup (metrics and dataset split) for the field.

Reading between the lines

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

Inference: the same divide-and-conquer recipe—shape-specific strip convolutions, dataset-level memory, and expert-fusion decoding—should transfer to other thin or hollow anatomical structures such as vessels or nerves; running the modules on a public vessel or nerve segmentation benchmark would test this.
Inference: because the memory bank is trained on the training set's global patterns, its benefit may depend on scanner protocol; a multi-center MRI evaluation would show whether the bank needs re-initialization when the data distribution shifts.
Inference: the memory-bank design suggests an incremental-learning use the paper does not explore—freeze the encoder and update only the bank as new cases arrive—which could matter for clinical deployment where data accumulate over time.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

Two things to know. First, this paper attacks a real gap: nobody has published an automated segmentation method for pelvic radiation injury, and the authors built an in-house dataset of 344 cases / 8k T2-MRI slices with manual annotations for injured and non-injured pelvic structures. That alone is a useful contribution. Second, the paper's central 'state-of-the-art' claim is not yet backed by the evidence: every number in Tables 1 and 2 is a single run, with no error bars, no multiple seeds, no significance tests, no code, and no public data.

The architecture is a coherent combination of existing ideas, not a conceptual leap. Strip convolutions come from SegNext, extended here with diagonal directions; the memory bank is adapted from space-time memory networks; the decoder is an MoE variant with cross-stage feature mixing. The clinical motivation—strip-like muscles and circular hollow organs—makes sense, and the modules are straightforward to follow.

The soft spot is the evaluation. The 2.5–2.8 DSC points over EMCAD in Table 1 could easily be seed noise in medical segmentation, and the ablation differences (1.3–2.9 points) have the same problem. Baseline hyperparameters are not specified, and the memory bank's capacity and sliding-window size are not stated, so even with code, the method would be hard to reproduce exactly. The dataset is private, so external verification is impossible right now. I don't see any internal inconsistency or circular reasoning; the issue is purely that the evidence is too weak to support the superiority claim.

Who's this for? People working on PRI or on segmentation of hollow/strip-like organs. It is a reasonable first attempt and a useful reference for the task, but I wouldn't treat its numbers as a benchmark yet.

I would send this to peer review, not desk reject it. The task is new and the fix is concrete: report multiple seeds with variance, run paired significance tests, release code and data, specify the hyperparameters, and tone down the SOTA claim until the comparisons are statistically defensible.

Referee Report

4 major / 5 minor

Summary. The paper proposes PDC-Net, a deep learning architecture for segmenting pelvic radiation injury (PRI) from T2-weighted MRI. The method combines three novel modules: a Multi-Direction Aggregation (MDA) module using strip convolutions in four directions, a Memory-Guided Context (MGC) module that maintains dataset-level memory to reduce background interference, and an Adaptive Fusion Decoder (AFD) based on a Mixture-of-Experts framework. The authors introduce the first large-scale private PRI dataset (344 cases, 8049 images) and report quantitative comparisons against six existing segmentation methods, along with ablation studies. The central claim is that PDC-Net achieves state-of-the-art performance on PRI segmentation.

Significance. If the reported improvements are reproducible, this paper would be a valuable contribution as the first AI-based method specifically designed for PRI segmentation, and the dataset itself is a significant resource for the community. The architectural ideas are well motivated by the strip-like and circular morphology of the target structures, and the paper compares against a reasonable set of recent baselines. The strengths are the clear problem framing, the large in-house dataset, and the modular design that allows each component to be ablated. However, the empirical evidence for the central claim is currently weak because all reported numbers come from single training runs with no error bars, no significance tests, and no external validation, leaving the reported 2–3 point DSC advantages within the range of run-to-run variability.

major comments (4)

[Section 3.2, Table 1] The claim that PDC-Net achieves state-of-the-art performance rests on single-run DSC differences of 2.52 points (negative class) and 2.82 points (positive class) over the strongest baseline EMCAD. No error bars, repeated seeds, or significance tests are reported. On a private dataset with no code release, these gaps are within the range of seed-to-seed variability commonly observed for medical segmentation networks, so the superiority claim is not yet established. Please report results over multiple seeds with paired significance tests (e.g., Wilcoxon signed-rank or paired bootstrap) and provide the variance.
[Section 3.2, Table 2] The ablation study is subject to the same single-run limitation. The claimed contributions of each module—MDA (2.61 points on the negative class vs. PConv), MGC (1.28 points vs. EFA), and AFD (2.88 points vs. U-shape)—are each based on one run, with no confidence intervals. Since these differences are comparable to typical seed-to-seed variability, the conclusion that each proposed module contributes a measurable gain is not supported. Please add repeated runs and statistical tests, or at least report per-seed results and the mean±std for each configuration.
[Section 3.1, Compared Methods] The comparison protocol is underspecified. The paper does not state whether all baselines were trained with the same hyperparameters, preprocessing, loss functions, and data splits, nor how the 3D method UNETR++ was adapted to the 2D slice-level task. Without this information, the observed improvements over baselines could reflect differences in training setup rather than architectural merit. Please document the exact training protocol for every baseline, including any modifications made for fair comparison.
[Section 3.1, Datasets] The evaluation uses a private in-house dataset, and no code or test-set predictions are released. While private clinical data are acceptable, the lack of any external verification, combined with the absence of error bars, makes the headline result impossible to validate. Please consider releasing pseudonymized features, test-set predictions, or a public benchmark subset, and at minimum provide a detailed description of the data-split procedure, annotation protocol, and any quality control steps.

minor comments (5)

[Section 2.1, Eq. (2)] In the text following Eq. (2), 'the final output of the MPA module' should be 'the final output of the MDA module'.
[Section 3.3, MGC ablation] In the paragraph for the MGC ablation, the sentence 'our AFD shows an improvement of 1.28%' should refer to the MGC module, not AFD.
[Section 2.2, Eqs. (3)–(4)] The dimensions in Eqs. (3)–(4) are not fully defined: after max and average pooling, what is the spatial size of Avg(f_patch) and Max(f_patch)? The symbol S in the memory bank is used without explicit definition, and the shapes in Eq. (4) appear inconsistent (element-wise operations between a FC output and the pooled features). Please clarify the tensor shapes for each step.
[Section 2.3, Eq. (7)] The text says 'we apply adaptive pooling' but Eq. (7) uses AvgPool. Please specify the pooling type and the output dimension before the FC layer.
[Table 2] The ablation tables report only DSC. Since Table 1 includes MCC, ACC, and HD, reporting at least MCC or HD in the ablations would provide a more complete picture of the contribution of each module.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: PDC-Net's claims are empirical comparisons against external baselines, not derivations reduced to their own inputs.

full rationale

PDC-Net makes no analytic prediction that reduces to a fitted parameter or to a self-citation. The central claim (Section 1: 'our approach achieves state-of-the-art performance') is supported by Table 1, which compares PDC-Net to six external methods (U-Net, UNet++, TransUNet, H2Former, UNETR++, EMCAD), and by Table 2 ablations that replace each proposed module with an alternative. The MDA, MGC, and AFD modules are architectural components trained end-to-end with cross-entropy and Dice losses; their outputs are not defined in terms of the evaluation metric, and the reported DSC values are measurements, not identities. The paper cites prior work by its own authors ([14] MoE-Polyp, [15] SAM2-UNet), but these citations are used only as background for well-established MoE and segmentation ideas and are not load-bearing for the SOTA claim. Weaknesses such as single-run evaluation without error bars are correctness or statistical-support concerns, not circularity. The derivation chain is self-contained in the sense that any circularity criterion requires a reduction of the claimed result to its inputs, and no such reduction appears in the text.

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

The claims rest primarily on a private dataset and unstated statistical assumptions. No independent evidence is provided for label quality or split representativeness, and key hyperparameters for the new modules are unreported.

free parameters (4)

Memory bank capacity K = unspecified
The MGC module stores dataset-level patterns in a bank M of size 1x1xSxK, but K is not reported or ablated; the module's behavior depends on it.
Sliding window size N = unspecified
MGC partitions features into N by N patches; N is not stated or ablated.
Loss weighting between cross-entropy and Dice = unspecified
Training uses both CE and Dice loss, but relative weights are not given; segmentation performance depends on this balance.
Training hyperparameters = lr=1e-4 to 1e-6, 100 epochs, batch 16, weight decay 5e-4
Hand-chosen settings (Section 3.1) that affect final accuracy; no sensitivity analysis is reported.

assumptions (4)

domain assumption Radiologist-delineated labels on T2 MRI are the correct ground truth for pelvic radiation injury.
Section 3.1 states radiologists manually delineated four structures and marked injured versus non-injured; if these labels are noisy, every reported metric inherits that noise.
domain assumption The 274/70 patient split is representative and unbiased.
Section 3.1 introduces the split without describing randomization, patient demographics, or scan protocols; distribution shift would invalidate the comparison.
domain assumption The four target structures (rectal intrinsic muscle, anal sphincter, puborectalis, full bladder) are the complete set of relevant ROIs.
Section 1 defines the ROI this way; if radiation injury occurs elsewhere, the model's clinical utility is incomplete.
standard math Standard deep learning training assumptions hold (gradient descent converges, no data leakage between train and test).
Implicit in any PyTorch training; no leakage verification is reported.

how reviews work

0 comments

Cite this review

Pith. "Pith review of PDC-Net: Pattern Divide-and-Conquer Network for Pelvic Radiation Injury Segmentation." pith.science (2026). https://pith.science/paper/OIX7NZQ5

@misc{pith2026250617712,
  author       = {Pith},
  title        = {Pith review of: PDC-Net: Pattern Divide-and-Conquer Network for Pelvic Radiation Injury Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OIX7NZQ5}},
  note         = {Machine review of arXiv:2506.17712}
}

read the original abstract

Accurate segmentation of Pelvic Radiation Injury (PRI) from Magnetic Resonance Images (MRI) is crucial for more precise prognosis assessment and the development of personalized treatment plans. However, automated segmentation remains challenging due to factors such as complex organ morphologies and confusing context. To address these challenges, we propose a novel Pattern Divide-and-Conquer Network (PDC-Net) for PRI segmentation. The core idea is to use different network modules to "divide" various local and global patterns and, through flexible feature selection, to "conquer" the Regions of Interest (ROI) during the decoding phase. Specifically, considering that our ROI often manifests as strip-like or circular-like structures in MR slices, we introduce a Multi-Direction Aggregation (MDA) module. This module enhances the model's ability to fit the shape of the organ by applying strip convolutions in four distinct directions. Additionally, to mitigate the challenge of confusing context, we propose a Memory-Guided Context (MGC) module. This module explicitly maintains a memory parameter to track cross-image patterns at the dataset level, thereby enhancing the distinction between global patterns associated with the positive and negative classes. Finally, we design an Adaptive Fusion Decoder (AFD) that dynamically selects features from different patterns based on the Mixture-of-Experts (MoE) framework, ultimately generating the final segmentation results. We evaluate our method on the first large-scale pelvic radiation injury dataset, and the results demonstrate the superiority of our PDC-Net over existing approaches.

Figures

Figures reproduced from arXiv: 2506.17712 by the authors.

**Figure 2.** The overall framework of our proposed PDC-Net, which consists of four [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** PRI segmentation results of different methods, where red pixels represent [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 12 canonical work pages

[1]

Medical Image Analysis97, 103280 (2024)

Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., et al.: Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis97, 103280 (2024)

2024
[2]

CA: A Cancer Journal for Clinicians 66(2), 115–132 (2016)

Chen, W., Zheng, R., Baade, P.D., Zhang, S., Zeng, H., Bray, F., Jemal, A., Yu, X.Q., He, J.: Cancer statistics in china, 2015. CA: A Cancer Journal for Clinicians 66(2), 115–132 (2016)

work page 2016
[3]

Advances in Neural Information Processing Systems35, 1140–1156 (2022)

Guo, M.H., Lu, C.Z., Hou, Q., Liu, Z., Cheng, M.M., Hu, S.M.: Segnext: Rethink- ing convolutional attention design for semantic segmentation. Advances in Neural Information Processing Systems35, 1140–1156 (2022)

work page 2022
[4]

IEEE Transactions on Medical Imaging42(9), 2763–2775 (2023)

He, A., Wang, K., Li, T., Du, C., Xia, S., Fu, H.: H2former: An efficient hierarchical hybrid transformer for medical image segmentation. IEEE Transactions on Medical Imaging42(9), 2763–2775 (2023)

work page 2023
[5]

Neural Computation3(1), 79–87 (1991)

Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation3(1), 79–87 (1991)

work page 1991
[6]

Chinese Journal of Gas- trointestinal Surgery24(11), 956–961 (2021)

Ma, T.: Interpretation of chinese expert consensus on multidisciplinary diagnosis and treatment of radiation rectal injury (2021 edition). Chinese Journal of Gas- trointestinal Surgery24(11), 956–961 (2021)

work page 2021
[7]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Oh, S.W., Lee, J.Y., Xu, N., Kim, S.J.: Video object segmentation using space-time memory networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9226–9235 (2019)

work page 2019
[8]

In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Ou, Y., Yuan, Y., Huang, X., Wong, S.T., Volpi, J., Wang, J.Z., Wong, K.: Patcher: Patchtransformerswithmixtureofexpertsforprecisemedicalimagesegmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 475–484. Springer (2022)

work page 2022

Show all 18 references

[9]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Rahman, M.M., Munir, M., Marculescu, R.: Emcad: Efficient multi-scale convolu- tional attention decoding for medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11769– 11779 (2024)

2024
[10]

In: Medical Image Computing and Computer-Assisted Intervention

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomed- ical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention. pp. 234–241. Springer (2015) 10 X. Xiong et al

2015
[11]

IEEE Transactions on Medical Imaging (2024)

Shaker, A.M., Maaz, M., Rasheed, H., Khan, S., Yang, M.H., Khan, F.S.: Un- etr++: delving into efficient and accurate 3d medical image segmentation. IEEE Transactions on Medical Imaging (2024)

2024
[12]

CA: a cancer journal for clinicians 71(3), 209–249 (2021)

Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F.: Global cancer statistics 2020: Globocan estimates of incidence and mor- tality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 71(3), 209–249 (2021)

2021
[13]

Computational Visual Media8(3), 415–424 (2022)

Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media8(3), 415–424 (2022)

2022
[14]

In: Proceedings of the 6th ACM International Conference on Multimedia in Asia

Wu, Z., Xiong, X., Chen, Y., Li, S., Chen, H.: Moe-polyp: Shifting more attention to small polyp segmentation via mixture-of-experts. In: Proceedings of the 6th ACM International Conference on Multimedia in Asia. pp. 1–1 (2024)

2024
[15]

arXiv preprint arXiv:2408.08870 (2024)

Xiong, X., Wu, Z., Tan, S., Li, W., Tang, F., Chen, Y., Li, S., Ma, J., Li, G.: Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation. arXiv preprint arXiv:2408.08870 (2024)

2024
[16]

In: International Conference on Learning Repre- sentations (2024)

Yan, H., Wu, M., Zhang, C.: Multi-scale representations by varying window atten- tion for semantic segmentation. In: International Conference on Learning Repre- sentations (2024)

2024
[17]

In: European Conference on Computer Vision

Yu, H., Cho, Y., Kang, B., Moon, S., Kong, K., Kang, S.J.: Embedding-free trans- former with inference spatial reduction for efficient semantic segmentation. In: European Conference on Computer Vision. pp. 92–110. Springer (2024)

2024
[18]

IEEE Transac- tions on Medical Imaging39(6), 1856–1867 (2019)

Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transac- tions on Medical Imaging39(6), 1856–1867 (2019)

2019

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Medical Image Analysis97, 103280 (2024)

Chen, J., Mei, J., Li, X., Lu, Y., Yu, Q., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., et al.: Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Medical Image Analysis97, 103280 (2024)

2024

[2] [2]

CA: A Cancer Journal for Clinicians 66(2), 115–132 (2016)

Chen, W., Zheng, R., Baade, P.D., Zhang, S., Zeng, H., Bray, F., Jemal, A., Yu, X.Q., He, J.: Cancer statistics in china, 2015. CA: A Cancer Journal for Clinicians 66(2), 115–132 (2016)

work page 2016

[3] [3]

Advances in Neural Information Processing Systems35, 1140–1156 (2022)

Guo, M.H., Lu, C.Z., Hou, Q., Liu, Z., Cheng, M.M., Hu, S.M.: Segnext: Rethink- ing convolutional attention design for semantic segmentation. Advances in Neural Information Processing Systems35, 1140–1156 (2022)

work page 2022

[4] [4]

IEEE Transactions on Medical Imaging42(9), 2763–2775 (2023)

He, A., Wang, K., Li, T., Du, C., Xia, S., Fu, H.: H2former: An efficient hierarchical hybrid transformer for medical image segmentation. IEEE Transactions on Medical Imaging42(9), 2763–2775 (2023)

work page 2023

[5] [5]

Neural Computation3(1), 79–87 (1991)

Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation3(1), 79–87 (1991)

work page 1991

[6] [6]

Chinese Journal of Gas- trointestinal Surgery24(11), 956–961 (2021)

Ma, T.: Interpretation of chinese expert consensus on multidisciplinary diagnosis and treatment of radiation rectal injury (2021 edition). Chinese Journal of Gas- trointestinal Surgery24(11), 956–961 (2021)

work page 2021

[7] [7]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Oh, S.W., Lee, J.Y., Xu, N., Kim, S.J.: Video object segmentation using space-time memory networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9226–9235 (2019)

work page 2019

[8] [8]

In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Ou, Y., Yuan, Y., Huang, X., Wong, S.T., Volpi, J., Wang, J.Z., Wong, K.: Patcher: Patchtransformerswithmixtureofexpertsforprecisemedicalimagesegmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 475–484. Springer (2022)

work page 2022