Pareto-Guided Optimization for Uncertainty-Aware Medical Image Segmentation

2026-01-27

AI Summary

This work addresses the challenge of high uncertainty in boundary regions during medical image segmentation, where conventional methods that uniformly optimize all pixels often suffer from training instability and fail to converge to a Pareto-optimal solution. To overcome this, the authors propose a region-based curriculum learning strategy that prioritizes learning from highly certain interior regions and progressively incorporates ambiguous boundaries. This approach is further enhanced by integrating a Pareto-consistent loss, region-adaptive loss reshaping, and a soft-labeling mechanism to effectively balance the learning dynamics across different regions. Evaluated on brain metastasis and non-metastasis segmentation tasks, the method consistently outperforms traditional hard-label approaches across all sub-regions, demonstrating improved convergence stability and segmentation accuracy.

Technology Category

Computer Vision: SegmentationMachine Learning: Calibration & Uncertainty QuantificationSearch and Optimization: Learning to Search

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• medical image segmentation
• uncertainty
• Pareto optimization
• boundary ambiguity
• non-uniform uncertainty

Innovation

• Pareto-guided optimization
• uncertainty-aware segmentation
• region-wise curriculum learning
• fuzzy labeling
• gradient stabilization