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MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation
1Department of Student Airs, Heilongjiang Nursing College, Harbin, China.
Introduction:
Medical image segmentation is essential for analyzing medical data, improving diagnostics, treatment planning, and research. However, current methods struggle with different imaging types, poor generalization, and rare structure detection.
Methods:
To address these issues, we propose MedFusion-TransNet, a novel multi-modal fusion approach utilizing transformer-based architectures. By integrating multi-scale feature encoding, attention mechanisms, and dynamic optimization, our method significantly enhances segmentation precision. Our method uses the Context-Aware Segmentation Network (CASNet) and Dynamic Region-Guided Optimization (DRGO) to enhance segmentation by focusing on key anatomical areas.
Results:
These innovations tackle challenges like imbalanced datasets, boundary delineation, and multi-modal complexity. Validation on benchmark datasets demonstrates substantial improvements in accuracy, robustness, and boundary precision, marking a significant step forward in segmentation technologies.
Discussion:
MedFusion-TransNet offers a transformative tool for advancing the quality and reliability of medical image analysis across diverse clinical applications.
