Fuzzy rough set loss for deep learning-based precise medical image segmentation.

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Abstract

Accurate segmentation of medical images is crucial for diagnosis and treatment planning, yet it remains challenging due to ambiguous lesion boundaries, class imbalance, and complex anatomical structures. We propose a novel Fuzzy Rough Set-inspired (FRS) loss function that addresses these challenges by integrating pixels' fuzzy similarity relations with a boundary uncertainty model in a convex combination method. To obtain the boundary uncertainty model, the fuzzy lower and upper approximations of a set of pixels and membership weights are utilized. The FRS loss function enhances boundary sensitivity and handles prediction uncertainty through its dual components: a fuzzy similarity term that captures gradual transitions at lesion boundaries, and boundary uncertainty model that deals with uncertainty and mitigates class imbalance. Extensive experiments across five diverse medical imaging datasets - breast ultrasound, gastrointestinal polyps, brain Magnetic Resonance Imaging (MRI), chest Computed Tomography (CT), and skin lesions - demonstrate the effectiveness of our approach. The FRS loss achieves superior segmentation performance with an average improvement of 2.1% in Dice score compared to the best baseline method, while demonstrating statistically significant improvements across all evaluated metrics (p < 0.001). The FRS loss shows its robustness to moderate class imbalance while maintaining computational efficiency (mean inference time 0.075-0.12 s per image, 4.5 MB memory). These results suggest that the FRS loss function provides a robust and interpretable framework for precise medical image segmentation, particularly in cases with ambiguous boundaries and moderate imbalance. Code: https://github.com/MohsinFurkh/Fuzzy-Rough-Set-Loss.

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