mlsanityData to 7 Oct 2026

Pareto-Guided Optimization for Uncertainty-Aware Medical Image Segmentation

arXiv 2601.19365 ↗First posted 27 Jan 2026cs.CVcited by 0

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Abstract (arXiv)

Uncertainty in medical image segmentation is inherently non-uniform, with boundary regions exhibiting substantially higher ambiguity than interior areas. Conventional training treats all pixels equally, leading to unstable optimization during early epochs when predictions are unreliable. We argue that this instability hinders convergence toward Pareto-optimal solutions and propose a region-wise curriculum strategy that prioritizes learning from certain regions and gradually incorporates uncertain ones, reducing gradient variance. Methodologically, we introduce a Pareto-consistent loss that balances trade-offs between regional uncertainties by adaptively reshaping the loss landscape and constraining convergence dynamics between interior and boundary regions; this guides the model toward Pareto-approximate solutions. To address boundary ambiguity, we further develop a fuzzy labeling mechanism that maintains binary confidence in non-boundary areas while enabling smooth transitions near boundaries, stabilizing gradients, and expanding flat regions in the loss surface. Experiments on brain metastasis and non-metastatic tumor segmentation show consistent improvements across multiple configurations, with our method outperforming traditional crisp-set approaches in all tumor subregions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 51 references that are in the map. First posted on arXiv 27 Jan 2026.

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References · 33

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  4. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  5. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  6. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  7. 2019When Does Label Smoothing Help?cited by 985
  8. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  9. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  10. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 816
  11. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 813
  12. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 809
21 more
  1. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  2. 2019Confident Learning: Estimating Uncertainty in Dataset Labelscited by 411
  3. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 278
  4. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  5. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 162
  6. 2019PHiSeg: Capturing Uncertainty in Medical Image Segmentationcited by 128
  7. 2016HeMIS: Hetero-Modal Image Segmentationcited by 102
  8. 2022mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentationcited by 94
  9. 2019Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentationcited by 75
  10. 2019Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentationcited by 68
  11. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  12. 2023Understanding metric-related pitfalls in image analysis validationcited by 57
  13. 2022Pareto Set Learning for Expensive Multi-Objective Optimizationcited by 51
  14. 2014Training Convolutional Networks with Noisy Labelscited by 47
  15. 2021Scalable Pareto Front Approximation for Deep Multi-Objective Learningcited by 45
  16. 2019Supervised Uncertainty Quantification for Segmentation with Multiple Annotationscited by 42
  17. 2021Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labelscited by 32
  18. 2023Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registrationcited by 14
  19. 2023A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applicationscited by 13
  20. 2024Trusted Multi-view Learning under Noisy Supervisioncited by 8
  21. 2024Provable Uncertainty Decomposition via Higher-Order Calibrationcited by 8

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