Pareto-Guided Optimization for Uncertainty-Aware Medical Image Segmentation

TL;DR

This paper introduces a Pareto-guided, region-wise curriculum learning approach with a novel loss and fuzzy labeling to improve uncertainty-aware medical image segmentation, especially at boundaries.

Contribution

It proposes a region-wise curriculum strategy, a Pareto-consistent loss, and fuzzy labeling mechanisms to enhance segmentation accuracy under uncertainty.

Findings

Consistent improvements on brain metastasis and tumor segmentation datasets.

Outperforms traditional crisp-set approaches across tumor subregions.

Reduces gradient variance and stabilizes training near uncertain boundaries.

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.