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arxiv: 2605.16519 · v1 · pith:YS3RSFLFnew · submitted 2026-05-15 · 💻 cs.CV · eess.SP

DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy

Zhuoyu Wu , Wenhui Ou , Lexi Zhang , Pei-Sze Tan , Dongjun Wu , Junhe Zhao , Wenqi Fang , Rapha\"el C.-W. Phan This is my paper

Pith reviewed 2026-05-20 18:15 UTC · model grok-4.3

classification 💻 cs.CV eess.SP
keywords polyp segmentationcolonoscopylightweight networkpseudo-depth guidancemulti-task learningreal-time inferencemedical image segmentationcross-dataset generalization
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The pith

Pseudo-depth guidance lets a tiny model segment polyps more accurately than models 20 times larger while running in real time.

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

The paper introduces DepthPolyp, a compact segmentation network that adds a pseudo-depth prediction task to help the model focus on polyp boundaries despite motion blur, reflections, and uneven lighting common in live colonoscopy. It pairs this multi-task setup with three lightweight design choices: hierarchical Ghost factorization to generate features cheaply, Interleaved Shuffle Fusion to mix information across scales at low cost, and Dynamic Group Gating to re-weight channels adaptively. Experiments show the resulting 3.57-million-parameter model generalizes from degraded training data to both clean and noisy test sets, beats other small models, and even surpasses much larger networks on real surgical video from PolypGen while exceeding 180 frames per second on mobile hardware. A sympathetic reader would care because reliable, instant polyp outlines during procedures could improve early cancer detection without requiring powerful computers in the operating room.

Core claim

DepthPolyp performs pseudo-depth-guided multi-task learning inside an efficient backbone that uses hierarchical Ghost factorization, Interleaved Shuffle Fusion, and Dynamic Group Gating; when trained on degraded images it delivers stronger cross-dataset generalization than other lightweight networks and remains competitive with far larger models, reaching superior segmentation accuracy on PolypGen surgical videos at real-time speed and under 1 GMAC.

What carries the argument

Pseudo-depth guided multi-task learning that supplies an auxiliary depth map to steer feature extraction, implemented through hierarchical Ghost factorization for compact feature generation, Interleaved Shuffle Fusion for low-cost cross-scale mixing, and Dynamic Group Gating for adaptive per-group weighting.

If this is right

  • Real-time polyp segmentation becomes feasible on standard clinical hardware without sacrificing accuracy in noisy conditions.
  • Training on artificially degraded images transfers better to live surgical video than training on clean benchmarks alone.
  • The same lightweight modules can be reused for other medical video tasks that must tolerate blur and reflections.
  • Deployment in resource-limited clinics becomes practical because inference stays above 180 FPS on mobile devices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The pseudo-depth auxiliary task could be added to segmentation pipelines for other endoscopic procedures such as bronchoscopy or gastroscopy.
  • Similar efficient fusion and gating blocks might improve real-time object detection in other domains with unstable lighting, such as underwater or automotive vision.
  • Combining the model with temporal tracking across video frames could further reduce false positives during rapid camera motion.

Load-bearing premise

That the pseudo-depth signal extracted from the same colonoscopy images will reliably help the network ignore motion blur, specular highlights, and lighting shifts that occur in actual clinical procedures.

What would settle it

A new collection of real surgical colonoscopy videos from different endoscopes or patient populations on which DepthPolyp no longer outperforms models many times larger or drops below real-time frame rates.

Figures

Figures reproduced from arXiv: 2605.16519 by Dongjun Wu, Junhe Zhao, Lexi Zhang, Pei-Sze Tan, Rapha\"el C.-W. Phan, Wenhui OU, Wenqi Fang, Zhuoyu Wu.

Figure 1
Figure 1. Figure 1: Overview of the proposed DepthPolyp framework. During training (upper-left), the input image is processed by DepthPolyp together with a frozen Depth-Anything v2 (Small) model to provide pseudo-depth supervision. DepthPolyp jointly predicts segmentation and auxiliary depth, while pseudo-depth is used only during training to encourage geometry-aware learning. The lightweight decoder (lower-left) integrates G… view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative results of DepthPolyp on sequential PolypGen frames (Sequence 22), showing input images, predicted polyp masks, and depth-aware representations [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison on challenging colonoscopy images affected by motion blur, illumination variation, low contrast, and specular highlights. Each row corre￾sponds to one test case. From left to right, the columns show the input image, reference annotation, predictions from representative baseline methods, and DepthPolyp (Ours). White denotes true positives, red false positives, and green false negative… view at source ↗
read the original abstract

Accurate polyp segmentation in colonoscopy is essential for early colorectal cancer detection, yet real-world clinical environments pose persistent challenges such as motion blur, specular reflections, and illumination instability. Most existing methods are optimized on clean benchmark images and suffer noticeable performance degradation when deployed in authentic surgical scenarios. We propose DepthPolyp, a lightweight and robust segmentation framework based on pseudo-depth-guided multi-task learning and efficient feature modulation. The architecture combines hierarchical Ghost factorization for compact feature generation, Interleaved Shuffle Fusion for low-cost cross-scale interaction, and Dynamic Group Gating for adaptive group-wise feature weighting. Extensive experiments demonstrate that DepthPolyp achieves strong cross-dataset generalization when trained on degraded data and evaluated on both clean and noisy target domains, consistently outperforming lightweight baselines and remaining competitive with substantially larger models. In real surgical video evaluation on PolypGen, DepthPolyp achieves better segmentation performance than models up to $20\times$ larger while preserving real-time inference speed. With only 3.57M parameters and 0.86 GMACs, the proposed method runs at over 180 FPS on mobile devices, making it well suited for real-time deployment in resource-constrained clinical environments. Code and pretrained weights are available at: https://github.com/ReaganWu/DepthPolyp/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces DepthPolyp, a lightweight polyp segmentation network for colonoscopy that uses pseudo-depth as an auxiliary task in a multi-task learning setup. The architecture integrates hierarchical Ghost factorization for compact features, Interleaved Shuffle Fusion for cross-scale interaction, and Dynamic Group Gating for adaptive weighting. The central claims are that training on degraded data yields strong generalization to clean and noisy domains, outperforming lightweight baselines while remaining competitive with models up to 20× larger on PolypGen real surgical videos, all while achieving real-time inference (>180 FPS) with 3.57M parameters and 0.86 GMACs.

Significance. If the robustness claims hold, the work would be significant for enabling practical, real-time polyp segmentation in authentic clinical environments where motion blur, specular reflections, and illumination changes are common. The focus on efficiency and open-sourcing of code and weights supports potential deployment in resource-constrained settings and improves reproducibility.

major comments (2)
  1. [Experiments] Experiments section: The manuscript reports strong cross-dataset and PolypGen results after training on degraded data, yet provides no quantitative metrics (e.g., depth estimation error or correlation) for pseudo-depth map quality on degraded versus clean frames. Without this, it is impossible to verify that the pseudo-depth signal remains informative under motion blur and specular reflections, which is required for the multi-task robustness claim to hold.
  2. [Ablation studies] Ablation studies: No experiment isolates the pseudo-depth branch by comparing the full model against an ablated version without depth guidance. This omission makes it difficult to attribute the reported gains on noisy target domains specifically to the pseudo-depth component rather than the efficient modules or training strategy alone.
minor comments (1)
  1. [Abstract] Abstract: While parameter count, GMACs, and FPS are stated, key segmentation metrics (Dice, IoU) on the primary benchmarks are not summarized, reducing the abstract's utility for quick assessment of performance claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: The manuscript reports strong cross-dataset and PolypGen results after training on degraded data, yet provides no quantitative metrics (e.g., depth estimation error or correlation) for pseudo-depth map quality on degraded versus clean frames. Without this, it is impossible to verify that the pseudo-depth signal remains informative under motion blur and specular reflections, which is required for the multi-task robustness claim to hold.

    Authors: We agree that quantitative metrics on pseudo-depth map quality would help substantiate the robustness claim. The pseudo-depth is produced by a fixed pre-trained estimator applied to both clean and degraded inputs without further fine-tuning. While the original submission focused on end-task segmentation metrics, we acknowledge the gap. In the revision we will add a table reporting depth estimation error (MAE and RMSE) and correlation coefficients between pseudo-depth maps and available ground-truth depth on both clean and synthetically degraded frames, directly addressing whether the auxiliary signal remains informative under the targeted degradations. revision: yes

  2. Referee: [Ablation studies] Ablation studies: No experiment isolates the pseudo-depth branch by comparing the full model against an ablated version without depth guidance. This omission makes it difficult to attribute the reported gains on noisy target domains specifically to the pseudo-depth component rather than the efficient modules or training strategy alone.

    Authors: We concur that isolating the pseudo-depth auxiliary task is necessary to attribute gains specifically to multi-task depth guidance. The original ablations examined the Ghost factorization, Interleaved Shuffle Fusion, and Dynamic Group Gating modules, but did not include a direct comparison with and without the depth branch. We will add this ablation in the revised manuscript, training an otherwise identical model without the pseudo-depth loss and reporting segmentation performance on both clean and noisy target domains to quantify the contribution of the depth guidance to cross-domain robustness. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical architecture with independent experimental validation

full rationale

The paper proposes an empirical neural architecture (DepthPolyp) combining pseudo-depth multi-task learning with named efficient modules (hierarchical Ghost factorization, Interleaved Shuffle Fusion, Dynamic Group Gating) and validates it via cross-dataset generalization tests and real-time FPS measurements on PolypGen. No mathematical derivation chain, uniqueness theorem, or prediction step is present; performance claims rest on direct empirical comparisons rather than any quantity that reduces to the model's own fitted parameters or self-citations by construction. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the effectiveness of pseudo-depth as an auxiliary signal and the listed architectural modules for handling real-world degradations; these are introduced without independent validation details in the abstract.

axioms (1)
  • domain assumption Pseudo-depth estimation supplies useful structural supervision that improves segmentation robustness under motion blur, specular reflections, and illumination changes.
    This is the core premise of the pseudo-depth guided multi-task learning described in the abstract.
invented entities (1)
  • Pseudo-depth map no independent evidence
    purpose: To provide auxiliary depth-like guidance for 2D polyp segmentation via multi-task learning.
    Introduced as a key component but no generation method or external validation is described in the abstract.

pith-pipeline@v0.9.0 · 5785 in / 1447 out tokens · 82937 ms · 2026-05-20T18:15:15.642886+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages · 3 internal anchors

  1. [1]

    Scientific Data10(1), 75 (2023)

    Ali,S.,Jha,D.,Ghatwary,N.,Realdon,S.,Cannizzaro,R.,Salem,O.E.,Lamarque, D., Daul, C., Riegler, M.A., Anonsen, K.V., et al.: A multi-centre polyp detection and segmentation dataset for generalisability assessment. Scientific Data10(1), 75 (2023)

    work page 2023

  2. [2]

    saliency maps from physicians

    Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilar- iño, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized medical imaging and graphics 43, 99–111 (2015)

    work page 2015

  3. [3]

    Pattern Recognition45(9), 3166–3182 (2012)

    Bernal, J., Sánchez, J., Vilarino, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recognition45(9), 3166–3182 (2012)

    work page 2012

  4. [4]

    TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

    Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.:Transunet:Transformersmakestrongencodersformedicalimagesegmentation. arXiv preprint arXiv:2102.04306 (2021)

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  5. [5]

    Medical Image Analysis97, 103241 (2024)

    Dai, D., Dong, C., Yan, Q., Sun, Y., Zhang, C., Li, Z., Xu, S.: I2u-net: A dual-path u-net with rich information interaction for medical image segmentation. Medical Image Analysis97, 103241 (2024)

    work page 2024

  6. [6]

    In: 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

    Dinh, B.D., Nguyen, T.T., Tran, T.T., Pham, V.T.: 1m parameters are enough? a lightweight cnn-based model for medical image segmentation. In: 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). pp. 1279–1284. IEEE (2023)

    work page 2023

  7. [7]

    In: International confer- ence on medical image computing and computer-assisted intervention

    Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Par- allel reverse attention network for polyp segmentation. In: International confer- ence on medical image computing and computer-assisted intervention. pp. 263–273. Springer (2020)

    work page 2020

  8. [8]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1580–1589 (2020)

    work page 2020

  9. [9]

    Annals of internal medicine177(7), 919–928 (2024)

    Hassan, C., Misawa, M., Rizkala, T., Mori, Y., Sultan, S., Facciorusso, A., An- tonelli, G., Spadaccini, M., Houwen, B.B., Rondonotti, E., et al.: Computer-aided diagnosis for leaving colorectal polyps in situ: a systematic review and meta- analysis. Annals of internal medicine177(7), 919–928 (2024)

    work page 2024

  10. [10]

    Jain, A., Sinha, S., Mazumdar, S.: Comparative analysis of machine learning frame- worksforautomaticpolypcharacterization.BiomedicalSignalProcessingandCon- trol95, 106451 (2024) DepthPolyp15

    work page 2024

  11. [11]

    In: International con- ference on multimedia modeling

    Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., De Lange, T., Johansen, D., Johansen, H.D.: Kvasir-seg: A segmented polyp dataset. In: International con- ference on multimedia modeling. pp. 451–462. Springer (2019)

    work page 2019

  12. [12]

    Journal of imaging8(6), 169 (2022)

    Karmakar, R., Nooshabadi, S.: Mobile-polypnet: Lightweight colon polyp segmen- tation network for low-resource settings. Journal of imaging8(6), 169 (2022)

    work page 2022

  13. [13]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7482–7491 (2018)

    work page 2018

  14. [14]

    Expert Systems with Applications 295, 128835 (2026)

    Li, J., Xu, Q., He, X., Liu, Z., Zhang, D., Wang, R., Qu, R., Qiu, G.: Cfformer: Cross cnn-transformer channel attention and spatial feature fusion for improved segmentation of heterogeneous medical images. Expert Systems with Applications 295, 128835 (2026)

    work page 2026

  15. [15]

    Depth Anything 3: Recovering the Visual Space from Any Views

    Lin, H., Chen, S., Liew, J.H., Chen, D.Y., Li, Z., Shi, G., Feng, J., Kang, B.: Depth anything 3: Recovering the visual space from any views. arXiv preprint arXiv:2511.10647 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  16. [16]

    Visual Intelligence3(1), 1 (2025)

    Mei, J., Zhou, T., Huang, K., Zhang, Y., Zhou, Y., Wu, Y., Fu, H.: A survey on deep learning for polyp segmentation: Techniques, challenges and future trends. Visual Intelligence3(1), 1 (2025)

    work page 2025

  17. [17]

    In: European Conference on Com- puter Vision

    Phuong, T.N., Duy, V.N., Sakaino, H.: Bbd-polyp: Weakly supervised polyp seg- mentation via bounding box and depth map. In: European Conference on Com- puter Vision. pp. 392–408. Springer (2024)

    work page 2024

  18. [18]

    In: International Conference on Medical image computing and computer-assisted intervention

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedi- cal image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)

    work page 2015

  19. [19]

    In: International conference on medical image computing and computer-assisted intervention

    Sun, J., Darbehani, F., Zaidi, M., Wang, B.: Saunet: Shape attentive u-net for interpretable medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. pp. 797–806. Springer (2020)

    work page 2020

  20. [20]

    In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    Taghavi, P., Langari, R., Pandey, G.: Swinmtl: A shared architecture for simultane- ous depth estimation and semantic segmentation from monocular camera images. In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 4957–4964. IEEE (2024)

    work page 2024

  21. [21]

    In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI)

    Tang, F., Ding, J., Quan, Q., Wang, L., Ning, C., Zhou, S.K.: Cmunext: An efficient medical image segmentation network based on large kernel and skip fusion. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI). pp. 1–5. IEEE (2024)

    work page 2024

  22. [22]

    In: International confer- ence on medical image computing and computer-assisted intervention

    Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: Gated axial-attention for medical image segmentation. In: International confer- ence on medical image computing and computer-assisted intervention. pp. 36–46. Springer (2021)

    work page 2021

  23. [23]

    In: International conference on medical image computing and computer-assisted intervention

    Valanarasu, J.M.J., Patel, V.M.: Unext: Mlp-based rapid medical image segmen- tation network. In: International conference on medical image computing and computer-assisted intervention. pp. 23–33. Springer (2022)

    work page 2022

  24. [24]

    IEEE Signal Processing Letters32, 3062–3066 (2025)

    Wang, P., Zhang, Z., Gao, G., Zhang, Y., Zheng, Z.: Agentpolyp: Accurate polyp segmentation via image enhancement agent. IEEE Signal Processing Letters32, 3062–3066 (2025)

    work page 2025

  25. [25]

    In: ICASSP 2026 - 2026 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP)

    Wu, Z., Ou, W., Tan, P.S., Yang, J., Fang, W., Wang, Z., Phan, R.C.W.: En- docaver: Handling fog, blur and glare in endoscopic images via joint deblurring- segmentation. In: ICASSP 2026 - 2026 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP). pp. 6981–6985 (2026)

    work page 2026

  26. [26]

    Computers in Biology and Medicine183, 109223 (2024)

    Wu, Z., Wu, Q., Fang, W., Ou, W., Wang, Q., Zhang, L., Chen, C., Wang, Z., Li, H.: Harmonizing unets: Attention fusion module in cascaded-unets for low-quality oct image fluid segmentation. Computers in Biology and Medicine183, 109223 (2024)

    work page 2024

  27. [27]

    IEEE Transactions on Cybernetics54(9), 5040–5053 (2024) 16 Z

    Xiao, B., Hu, J., Li, W., Pun, C.M., Bi, X.: Ctnet: Contrastive transformer net- work for polyp segmentation. IEEE Transactions on Cybernetics54(9), 5040–5053 (2024) 16 Z. Wu et al

    work page 2024

  28. [28]

    Advances in neural information processing systems34, 12077–12090 (2021)

    Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in neural information processing systems34, 12077–12090 (2021)

    work page 2021

  29. [29]

    Depth Anything V2

    Yang, L., Kang, B., Huang, Z., Zhao, Z., Xu, X., Feng, J., Zhao, H.: Depth anything v2. arXiv:2406.09414 (2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  30. [30]

    Pattern Recognition154, 110554 (2024)

    Yu, Z., Zhao, L., Liao, T., Zhang, X., Chen, G., Xiao, G.: A novel non-pretrained deep supervision network for polyp segmentation. Pattern Recognition154, 110554 (2024)

    work page 2024

  31. [31]

    IEEE Signal Processing Letters (2024)

    Zheng, Z., Wu, C., Jin, Y., Jia, X.: Polyp-dam: Polyp segmentation via depth anything model. IEEE Signal Processing Letters (2024)

    work page 2024

  32. [32]

    Information Fusion108, 102392 (2024)

    Zhou, W., Cai, Y., Dong, X., Qiang, F., Qiu, W.: Adrnet-s*: Asymmetric depth registration network via contrastive knowledge distillation for rgb-d mirror seg- mentation. Information Fusion108, 102392 (2024)

    work page 2024

  33. [33]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Zhu, S., Brazil, G., Liu, X.: The edge of depth: Explicit constraints between seg- mentation and depth. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 13116–13125 (2020)

    work page 2020