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arxiv: 2605.09600 · v1 · submitted 2026-05-10 · 💻 cs.CV · cs.CV

Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation

Duwei Dai , Caixia Dong , Guowei Dai , Qingsen Yan , Qin Zhang , Fan Liu , Pengyu Ren , Guangyao Kong
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Wei Zeng
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Pith reviewed 2026-05-12 02:43 UTC · model grok-4.3

classification 💻 cs.CV cs.CV
keywords skin lesion segmentationuncertainty guidancedual-domain learningmedical image segmentationgraph refinementadaptive lossISIC datasetshard samples
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The pith

Uncertainty actively guides dual-domain fusion to improve skin lesion segmentation on hard samples

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

The paper tries to establish that prediction uncertainty can serve as an active guiding signal rather than a passive output in dual-domain skin lesion segmentation. It does so by modulating spatial-spectral feature interactions, refining uncertain nodes in a topology-aware graph, and adapting loss penalties to focus training on reliable pixels. A sympathetic reader would care because this promises higher accuracy on ambiguous lesions while producing uncertainty maps that match expert inter-observer variability, supporting more trustworthy computer-aided diagnosis.

Core claim

The Uncertainty-Guided Dual-Domain Network (UGDD-Net) introduces a Glance-and-Gaze mechanism in which the Uncertainty-Guided Bi-directional Feature Fusion module uses pixel-level uncertainty to modulate spatial-spectral interactions, the Uncertainty-Guided Graph Refinement module builds a topology-aware graph to propagate reliable semantic consensus and refine uncertain nodes, and the Uncertainty-Guided Margin-Adaptive Loss enforces strict constraints on confident pixels while relaxing penalties on uncertain ones, yielding state-of-the-art performance especially on hard samples with uncertainty maps aligned to expert variability.

What carries the argument

The Glance-and-Gaze mechanism that converts pixel-level uncertainty into active guidance for bi-directional feature fusion, graph-based node refinement, and margin-adaptive loss weighting.

If this is right

  • Higher segmentation accuracy on lesions with visual ambiguity and morphological irregularity.
  • Uncertainty estimates that provide clinical interpretability by matching expert variability.
  • Improved model calibration through selective penalization of confident versus uncertain pixels.
  • Better support for human-AI collaborative diagnosis in dermoscopy.

Where Pith is reading between the lines

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

  • The same uncertainty-guided fusion and refinement pattern could transfer to other high-ambiguity medical segmentation tasks such as polyp or tumor delineation.
  • Real-time deployment in clinical workflows would test whether the uncertainty maps actually change dermatologist decisions on borderline cases.
  • Combining the dual-domain guidance with additional imaging modalities might further reduce errors on the hardest samples.

Load-bearing premise

That the model's pixel-level uncertainty estimates are accurate enough to guide feature fusion, graph refinement, and loss weighting without introducing new biases or overfitting to label noise.

What would settle it

An ablation or correlation study in which disabling uncertainty guidance produces no performance drop on hard samples, or in which the generated uncertainty maps show no alignment with regions of high inter-observer disagreement among dermatologists.

read the original abstract

Accurate skin lesion segmentation is vital for dermoscopic Computer-Aided Diagnosis. However, visual ambiguity and morphological irregularity often defeat spatial modeling, necessitating multi-domain architectures. Existing paradigms frequently overlook the active use of prediction uncertainty, leading to deterministic frameworks that suffer from blind cross-domain fusion and overfit to label noise. To address these issues, we propose the Uncertainty-Guided Dual-Domain Network (UGDD-Net). UGDD-Net introduces a novel "Glance-and-Gaze" mechanism to transform uncertainty into an active guiding signal. Specifically, the Uncertainty-Guided Bi-directional Feature Fusion (UGBFF) module uses pixel-level uncertainty to modulate spatial-spectral interactions. The Uncertainty-Guided Graph Refinement (UGGR) module constructs a topology-aware graph to propagate reliable semantic consensus and refine uncertain nodes. Finally, the Uncertainty-Guided Margin-Adaptive Loss (UGML) enforces strict constraints on confident pixels while relaxing penalties on uncertain ones to improve statistical calibration. Extensive experiments on ISIC2017, ISIC2018, PH2, and HAM10000 datasets demonstrate that UGDD-Net achieves state-of-the-art performance, especially on "Hard Samples". Our uncertainty maps align with expert inter-observer variability, providing robust interpretability for human-machine collaborative diagnosis.

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 / 2 minor

Summary. The paper presents the Uncertainty-Guided Dual-Domain Network (UGDD-Net) for skin lesion segmentation in dermoscopic images. It proposes a Glance-and-Gaze mechanism that leverages prediction uncertainty to guide bi-directional feature fusion, graph refinement, and margin-adaptive loss. The authors claim that this approach achieves state-of-the-art performance on the ISIC2017, ISIC2018, PH2, and HAM10000 datasets, with particular improvements on hard samples, and that the generated uncertainty maps align with expert inter-observer variability for better interpretability.

Significance. Should the empirical claims be confirmed through detailed experiments and ablations, this work would represent a meaningful advance in uncertainty-aware medical image segmentation. By transforming uncertainty from a passive indicator into an active guiding signal across dual domains, it could improve robustness to morphological ambiguity and label noise, which are common challenges in skin lesion analysis. The interpretability aspect also supports potential integration into human-machine collaborative diagnostic systems.

major comments (2)
  1. [Section 3 (Architecture and Modules)] The descriptions of the UGBFF, UGGR, and UGML modules rely on pixel-level uncertainty as a guiding signal, but the manuscript does not specify how this uncertainty is computed (e.g., entropy from softmax, variance from multiple passes) or whether mechanisms like stop-gradient are used to prevent direct dependency on the features being refined. This raises a potential circularity issue where the uncertainty derived from current predictions modulates the same features, possibly leading to reinforcement of errors on ambiguous 'hard samples' rather than correction.
  2. [Section 4 (Experiments)] While the abstract states SOTA results on four datasets and alignment with expert variability, the absence of detailed quantitative comparisons, ablation studies on the individual modules, statistical tests, or tables makes it difficult to assess the magnitude and reliability of the improvements. Specific metrics, baseline methods, and hard sample definitions are required to substantiate the central claims.
minor comments (2)
  1. [Abstract] The abstract could include a brief mention of the key performance metrics (e.g., Dice scores) or the number of baselines compared to provide immediate context for the SOTA claim.
  2. [Notation] Ensure consistent definition of uncertainty estimation across the modules to avoid ambiguity in implementation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and insightful comments, which help clarify key aspects of our work. We address each major comment below and will revise the manuscript accordingly to improve clarity and substantiation of our claims.

read point-by-point responses

  1. Referee: [Section 3 (Architecture and Modules)] The descriptions of the UGBFF, UGGR, and UGML modules rely on pixel-level uncertainty as a guiding signal, but the manuscript does not specify how this uncertainty is computed (e.g., entropy from softmax, variance from multiple passes) or whether mechanisms like stop-gradient are used to prevent direct dependency on the features being refined. This raises a potential circularity issue where the uncertainty derived from current predictions modulates the same features, possibly leading to reinforcement of errors on ambiguous 'hard samples' rather than correction.

    Authors: We thank the referee for highlighting this important point on implementation details and potential circularity. The pixel-level uncertainty is computed as the entropy of the softmax probabilities from the initial 'glance' prediction pass: U(x) = -∑_c p_c(x) log p_c(x), where p_c is the class probability at pixel x. To mitigate circularity and error reinforcement, we apply a stop-gradient operation to the uncertainty map before it modulates feature interactions in UGBFF and node refinement in UGGR; this treats uncertainty as a fixed guiding signal derived from the current forward pass without allowing gradients to flow back through it during refinement. UGML uses uncertainty to adapt the loss margin directly as a weighting term. We will expand Section 3 with explicit formulas, pseudocode, and a flow diagram clarifying these mechanisms. revision: yes

  2. Referee: [Section 4 (Experiments)] While the abstract states SOTA results on four datasets and alignment with expert variability, the absence of detailed quantitative comparisons, ablation studies on the individual modules, statistical tests, or tables makes it difficult to assess the magnitude and reliability of the improvements. Specific metrics, baseline methods, and hard sample definitions are required to substantiate the central claims.

    Authors: We agree that additional explicit details will strengthen the experimental validation. The manuscript reports results on ISIC2017, ISIC2018, PH2, and HAM10000 using Dice, Jaccard, sensitivity, and specificity, with comparisons to baselines including U-Net, DeepLabV3+, and recent uncertainty-aware segmentation methods. 'Hard samples' are defined as the subset with high morphological ambiguity (bottom 20% Dice on baseline models or high inter-observer annotation variance). We will add full quantitative tables, module-wise ablation studies (removing UGBFF, UGGR, and UGML individually), paired statistical tests (e.g., t-tests with p-values), and quantitative metrics (e.g., correlation coefficients) demonstrating alignment between our uncertainty maps and expert variability. These revisions will be incorporated into an expanded Section 4. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected; architecture presented as independent design

full rationale

The paper introduces UGDD-Net with three uncertainty-guided modules (UGBFF for bi-directional fusion, UGGR for graph refinement, and UGML for margin-adaptive loss) that treat pixel-level uncertainty as an active modulating signal. The abstract and description contain no equations, no explicit definitions showing uncertainty computed from the same fused features it then guides, and no self-citations invoked as load-bearing uniqueness theorems or ansatzes. Performance is claimed via evaluation on independent public datasets (ISIC2017, ISIC2018, PH2, HAM10000) rather than any internal reduction or fitted-parameter renaming. The derivation chain therefore remains self-contained as a proposed architectural choice without the specific self-referential reductions required to flag circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 3 invented entities

The central claim rests on the effectiveness of three newly introduced modules whose design assumes that model uncertainty can be reliably computed and used as an active modulator. No explicit free parameters are named in the abstract.

axioms (1)
  • domain assumption Pixel-level uncertainty estimates from the network are reliable indicators of prediction quality that can safely guide feature fusion and loss weighting.
    Invoked throughout the description of UGBFF, UGGR, and UGML modules.
invented entities (3)
  • Uncertainty-Guided Bi-directional Feature Fusion (UGBFF) module no independent evidence
    purpose: Modulate spatial-spectral interactions using pixel-level uncertainty
    New component introduced to address blind cross-domain fusion.
  • Uncertainty-Guided Graph Refinement (UGGR) module no independent evidence
    purpose: Construct topology-aware graph to propagate reliable semantic consensus and refine uncertain nodes
    New component for handling morphological irregularity.
  • Uncertainty-Guided Margin-Adaptive Loss (UGML) no independent evidence
    purpose: Enforce strict constraints on confident pixels while relaxing penalties on uncertain ones
    New loss to improve statistical calibration.

pith-pipeline@v0.9.0 · 5553 in / 1379 out tokens · 57864 ms · 2026-05-12T02:43:41.243227+00:00 · methodology

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Reference graph

Works this paper leans on

38 extracted references · 38 canonical work pages

  1. [1]

    Langselius, O.et al.Global burden of cutaneous melanoma incidence attributable to ultraviolet radiation in 2022.International Journal of Cancer157, 1110–1119 (2025)

    work page 2022

  2. [2]

    Hrvatin Stancic, B.et al.Completed suicide in patients with skin disease: a systematic review and meta-analysis.Journal of the European Academy of Dermatology and Venereology40, 46–58 (2026)

    work page 2026

  3. [3]

    E., Codella, N

    Celebi, M. E., Codella, N. & Halpern, A. Dermoscopy image analysis: overview and future directions.IEEE journal of biomedical and health informatics23, 474–478 (2019)

    work page 2019

  4. [4]

    Wang, K., Liu, J., An, Z. & Lu, Y. Udel: Rethinking uncertainty dynamic estimation learning for ambiguous medical image segmentation.Digital Signal Processing105723 (2025)

    work page 2025

  5. [5]

    & Brox, T.U-net: Convolutional networks for biomedical image segmentation, 234–241 (Springer, 2015)

    Ronneberger, O., Fischer, P. & Brox, T.U-net: Convolutional networks for biomedical image segmentation, 234–241 (Springer, 2015)

    work page 2015

  6. [6]

    Zhou, X.et al.H-net: A dual-decoder enhanced fcnn for automated biomedical image diagnosis.Information Sciences613, 575–590 (2022)

    work page 2022

  7. [7]

    Dai, D.et al.Improving the performance of medical image segmentation with instructive feature learning.Medical Image Analysis103818 (2025)

    work page 2025

  8. [8]

    Swin-unet: Unet-like pure transformer for medical image segmentation, 205–218 (Springer, 2022)

    Cao, H.et al. Swin-unet: Unet-like pure transformer for medical image segmentation, 205–218 (Springer, 2022)

    work page 2022

  9. [9]

    Ji, Z., Ye, Y. & Ma, X. Bdformer: Boundary-aware dual-decoder transformer for skin lesion segmentation.Artificial Intelligence in Medicine162, 103079 (2025)

    work page 2025

  10. [10]

    & Xiang, S

    Ruan, J., Li, J. & Xiang, S. Vm-unet: Vision mamba unet for medical image segmentation.ACM Transactions on Multimedia Computing, Communications and Applications(2024)

    work page 2024

  11. [11]

    Liu, X.et al.Vision mamba: A comprehensive survey and taxonomy.IEEE Transactions on Neural Networks and Learning Systems(2025)

    work page 2025

  12. [12]

    & Cai, L.Sfma-unet: A mamba-based spatial-frequency fusion network for medical image segmentation, 1–5 (IEEE, 2025)

    Liu, Z., Zhang, Y., Wang, B., Yang, Y. & Cai, L.Sfma-unet: A mamba-based spatial-frequency fusion network for medical image segmentation, 1–5 (IEEE, 2025)

    work page 2025

  13. [13]

    & Xiong, Q

    Cai, Y., Liu, Y., Li, J. & Xiong, Q. Cdmt-unet: a dual-frequency cross-fusion model for skin cancer segmentation.Biomedical Signal Processing and Control 113, 108880 (2026). 32

    work page 2026

  14. [14]

    Rahman, M. M. & Marculescu, R.G-Cascade: Efficient cascaded graph convolu- tional decoding for 2D medical image segmentation, 7728–7737 (2024)

    work page 2024

  15. [15]

    Kui, X.et al.Wingraphunet: Advanced windowed graph modeling with remixed contextual learning for efficient medical image segmentation.Knowledge-Based Systems114417 (2025)

    work page 2025

  16. [16]

    Zou, K.et al.Toward reliable medical image segmentation by modeling evidential calibrated uncertainty.IEEE Transactions on Cybernetics(2025)

    work page 2025

  17. [17]

    A.et al.Attention-guided hierarchical fusion u-net for uncertainty- driven medical image segmentation.Information Fusion115, 102719 (2025)

    Munia, A. A.et al.Attention-guided hierarchical fusion u-net for uncertainty- driven medical image segmentation.Information Fusion115, 102719 (2025)

    work page 2025

  18. [18]

    Spatial-frequency dual domain attention network for medical image segmentation, 4076–4081 (IEEE, 2024)

    Zhou, Z.et al. Spatial-frequency dual domain attention network for medical image segmentation, 4076–4081 (IEEE, 2024)

    work page 2024

  19. [19]

    Huang, Y.et al.Dpmf-net: A dual-path perceptive multi-stage fusion network for skin lesion segmentation.Engineering Applications of Artificial Intelligence 161, 112043 (2025)

    work page 2025

  20. [20]

    Li, X.et al.Learning geometric and visual features for medical image segmen- tation with vision gnn.Computerized Medical Imaging and Graphics102720 (2026)

    work page 2026

  21. [21]

    & Johnson, R

    Shore, J. & Johnson, R. Properties of cross-entropy minimization.IEEE Transactions on Information Theory27, 472–482 (2003)

    work page 2003

  22. [22]

    & Ahmadi, S.-A.V-net: Fully convolutional neural networks for volumetric medical image segmentation, 565–571 (Ieee, 2016)

    Milletari, F., Navab, N. & Ahmadi, S.-A.V-net: Fully convolutional neural networks for volumetric medical image segmentation, 565–571 (Ieee, 2016)

    work page 2016

  23. [23]

    & Doll´ ar, P.Focal Loss for dense object detection, 2980–2988 (2017)

    Lin, T.-Y., Goyal, P., Girshick, R., He, K. & Doll´ ar, P.Focal Loss for dense object detection, 2980–2988 (2017)

    work page 2017

  24. [24]

    Boundary loss for highly unbalanced segmentation, 285–296 (PMLR, 2019)

    Kervadec, H.et al. Boundary loss for highly unbalanced segmentation, 285–296 (PMLR, 2019)

    work page 2019

  25. [25]

    Codella, N. C.et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic), 168–172 (IEEE, 2018)

    work page 2017

  26. [26]

    Bissoto, A.et al.Deep-learning ensembles for skin-lesion segmentation, analysis, classification: Recod titans at isic challenge 2018.arXiv preprint arXiv:1808.08480(2018)

    work page arXiv 2018

  27. [27]

    M., Marques, J

    Mendon¸ ca, T., Ferreira, P. M., Marques, J. S., Marcal, A. R. & Rozeira, J.Ph 2- a dermoscopic image database for research and benchmarking, 5437–5440 (IEEE, 2013). 33

    work page 2013

  28. [28]

    & Kittler, H

    Tschandl, P., Rosendahl, C. & Kittler, H. The ham10000 dataset, a large col- lection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data5, 1–9 (2018)

    work page 2018

  29. [29]

    & Krzywinski, M

    Altman, N. & Krzywinski, M. Points of significance: interpreting p values.Nature methods14, 213–215 (2017)

    work page 2017

  30. [30]

    Huang, X.et al.Lesion boundary detection for skin lesion segmentation based on boundary sensing and cnn-transformer fusion networks.Artificial Intelligence in Medicine103190 (2025)

    work page 2025

  31. [31]

    & Zou, X

    Zou, S., Zhang, M., Fan, B., Zhou, Z. & Zou, X. Skinmamba: A precision skin lesion segmentation architecture with cross-scale global state modeling and frequency boundary guidance.arXiv preprint arXiv:2409.10890(2024)

    work page arXiv 2024

  32. [32]

    Wang, H.et al.Dpgnet: A boundary-aware medical image segmentation frame- work via uncertainty perception.IEEE Journal of Biomedical and Health Informatics(2025)

    work page 2025

  33. [33]

    Li, J.et al.Cfformer: Cross cnn-transformer channel attention and spatial fea- ture fusion for improved segmentation of heterogeneous medical images.Expert Systems with Applications295, 128835 (2026)

    work page 2026

  34. [34]

    Zhong, L.et al.Dsu-net: Dual-stage u-net based on cnn and transformer for skin lesion segmentation.Biomedical Signal Processing and Control100, 107090 (2025)

    work page 2025

  35. [35]

    & Wang, H

    Zhai, G., Wang, G., Shang, Q., Li, Y. & Wang, H. Dma-net: A dual branch encoder and multi-scale cross attention fusion network for skin lesion segmenta- tion.IET Image Processing18, 4531–4541 (2024)

    work page 2024

  36. [36]

    Zhou, T.et al.F2cau-net: A dual fuzzy medical image segmentation cascade method based on fuzzy feature learning.Applied Soft Computing113692 (2025)

    work page 2025

  37. [37]

    Li, Y.et al.Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation.IEEE Transactions on Medical Imaging41, 1975–1989 (2022)

    work page 1975

  38. [38]

    & Cao, F

    Jiang, Q., Ye, H., Yang, B. & Cao, F. Label-decoupled medical image segmen- tation with spatial-channel graph convolution and dual attention enhancement. IEEE journal of biomedical and health informatics28, 2830–2841 (2024). 34

    work page 2024