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

• URL: http://arxiv.org/abs/2605.09600v1
• Date: Sun, 10 May 2026 15:20:58 GMT
• Title: Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation
• Abstract summary: We propose the Uncertainty-Guided Dual-Domain Network (UGDD-Net) for skin lesion segmentation.<n> UGDD-Net introduces a novel "Glance-and-Gaze" mechanism to transform uncertainty into an active guiding signal.<n>Our uncertainty maps align with expert inter-observer variability, providing robust interpretability for human-machine collaborative diagnosis.
• Score: 22.691374446857463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation [5.9165058427687045]
  We propose QUAM-SM, a post-hoc framework using targeted adversarial search to identify "adrially fragile" pixels.<n>By actively seeking perturbations that expose predictive instability, our method highlights regions where decisions are most vulnerable to being flipped.<n>Experiments on two public datasets with multiple expert annotations demonstrate that QUAM-SM outperforms both standard and recent uncertainty estimation approaches.
  arXiv  Detail & Related papers  (2026-06-17T09:42:24Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• UnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentation [16.902729686979303]
  Uncertainty-Prompted Feature Modulator (UPFM) treats aleatoric uncertainty as an active visual prompt rather than a mere output.<n>UnGAP balances superior segmentation accuracy with real-time inference speed.
  arXiv  Detail & Related papers  (2026-05-04T09:18:57Z)
• UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation [8.273126013629057]
  We propose an Uncertainty-Aware Hypergraph Refinement Network (UHR-Net)<n>We introduce an Uncertainty-Oriented Instance Contrastive (UO-IC) pretraining strategy that couples geometry-aware copy-paste augmentation with hard-negative mining of lesion-like background regions.<n>Second, we design an Uncertainty-Guided Hypergraph Refinement (UGHR) block, which derives an entropy-based uncertainty map from a coarse probability map to guide hypergraph refinement.
  arXiv  Detail & Related papers  (2026-04-30T16:38:51Z)
• Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection [1.8995711908407733]
  This study introduces a causally-constrained probabilistic forecasting framework for anomaly detection.<n>It integrates an explicit time-lagged causal graph prior with deep sequence modeling.<n>Experiments indicate that the proposed method achieves state-of-the-art detection performance.
  arXiv  Detail & Related papers  (2026-04-20T09:24:28Z)
• Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models [56.29123284262618]
  This work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty.<n>We analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class.<n>Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; and (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability
  arXiv  Detail & Related papers  (2026-04-13T03:59:54Z)
• Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts [80.32933059529135]
  Test-Time Adaptation (TTA) methods have emerged to adapt to target distributions during inference.<n>We propose Dual Uncertainty Optimization (DUO), the first TTA framework designed to jointly minimize both uncertainties for robust M3OD.<n>In parallel, we design a semantic-aware normal field constraint that preserves geometric coherence in regions with clear semantic cues.
  arXiv  Detail & Related papers  (2025-08-28T07:09:21Z)
• Uncertainty Guided Refinement for Fine-Grained Salient Object Detection [35.6505339733396]
  We introduce the uncertainty guidance learning approach to salient object detection (SOD)<n>We design a novel Uncertainty Guided Refinement Attention Network (UGRAN)<n> Experiments on seven benchmark datasets demonstrate the superiority of the proposed UGRAN over the state-of-the-art methodologies.
  arXiv  Detail & Related papers  (2025-04-13T17:34:37Z)
• A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection [60.09453163562244]
  We propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD.<n>In the estimation module, we design a novel label-free heterophily metric called HALO, which captures the critical graph properties for GFD.<n>In the alignment-based fraud detection module, we develop a joint-GNN architecture with ranking loss and asymmetric alignment loss.
  arXiv  Detail & Related papers  (2025-02-18T22:07:36Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• CRAUM-Net: Contextual Recursive Attention with Uncertainty Modeling for Salient Object Detection [0.0]
  We present a novel framework that integrates multi-scale context aggregation, advanced attention mechanisms, and an uncertainty-aware module for improved SOD performance.<n>Our Adaptive Cross-Scale Context Module effectively fuses features from multiple levels, leveraging Recursive Channel Spatial Attention and Convolutional Block Attention.<n>To train our network robustly, we employ a combination of boundary-sensitive and topology-preserving loss functions, including Boundary IoU, Focal Tversky, and Topological Saliency losses.
  arXiv  Detail & Related papers  (2020-06-04T18:33:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.