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

Mohsin Furkh Dar1, Avatharam Ganivada2

  • 1School of Computer Science, UPES, Dehradun, 248007, India.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|January 25, 2026
PubMed
Summary
This summary is machine-generated.

A new Fuzzy Rough Set-inspired (FRS) loss function improves medical image segmentation by enhancing boundary sensitivity and handling uncertainty. This method significantly boosts accuracy across diverse datasets, offering a robust solution for precise segmentation tasks.

Keywords:
Boundary detectionDeep learningFuzzy rough setsLoss functionMedical image segmentation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate medical image segmentation is vital for diagnosis and treatment planning.
  • Challenges include ambiguous lesion boundaries, class imbalance, and complex anatomy.
  • Existing methods struggle with precise boundary delineation and data imbalance.

Purpose of the Study:

  • To introduce a novel Fuzzy Rough Set-inspired (FRS) loss function for enhanced medical image segmentation.
  • To address challenges of ambiguous boundaries and class imbalance.
  • To improve the accuracy and robustness of segmentation models.

Main Methods:

  • Developed a Fuzzy Rough Set-inspired (FRS) loss function integrating fuzzy similarity relations and a boundary uncertainty model.
  • Utilized fuzzy lower/upper approximations and membership weights for the boundary uncertainty model.
  • Employed a convex combination method to merge the fuzzy similarity and boundary uncertainty components.

Main Results:

  • Achieved superior segmentation performance with an average 2.1% Dice score improvement over baseline methods.
  • Demonstrated statistically significant improvements across all evaluated metrics (p < 0.001).
  • Showed robustness to moderate class imbalance and maintained computational efficiency (0.075-0.12s inference time, 4.5MB memory).

Conclusions:

  • The FRS loss function provides a robust and interpretable framework for precise medical image segmentation.
  • It effectively handles ambiguous boundaries and moderate class imbalance.
  • The approach shows significant potential for improving diagnostic and treatment planning accuracy.