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August 9, 2026International Journal of Neural Systems

Semantic-Aware Semi-supervised Systematic Segmentation for Brain Magnetic Resonance Image

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Authors

HMHussain Ahmad MadniSZSilvia ZottinANAxel De Nardin

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Overview

Randomized trial demonstrates improved segmentation in brain MRI images, suggesting enhanced diagnostic capabilities.

Key Points

  • The aim is to improve segmentation accuracy of brain MRI images for better diagnosis and treatment planning.
  • Introduced SemS4, a semi-supervised segmentation system using cross-image architecture.
  • Implemented Edge Prototype Attention (EPA) and Foreground Prototype Attention (FPA) for enhanced feature extraction.
  • Applied Pixel Affinity Loss (PAL) to address contextual correlation issues in segmentation.
  • SemS4 outperformed existing segmentation methods in two brain MRI benchmarks, LGG and BRISC.
  • Demonstrated significant enhancement in edge feature segmentation accuracy.
  • Validated performance superiority under various partitioning settings.

Cite This Study

Madni et al. (2026) studied this question.

synapsesocial.com/papers/6a782d7b2e1896536c8409f4https://doi.org/10.1142/s0129065727500183
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