ArticleScientific reports2025
Medical image segmentation by combining feature enhancement Swin Transformer and UperNet.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- CardioSynergyNet: A Closed-Loop Multi-Task Deep Learning Architecture for Cardiac Segmentation and Biomarkers with Diagnosis from Paired ED-ES Cine-MRI.Tomography (Ann Arbor, Mich.) · 2026Article
- Twelve Fused Models by Fusing Four Types of Transformer-Based Tuners on Three Base UNet Architectures for Carotid Wall Segmentation and Plaque Burden/Intima-Media Thickness Measurements in Ultrasound Scans: A Scientific Validation Study.Diagnostics (Basel, Switzerland) · 2026Article
- Autodidactic dense anatomical models.Medical image analysis · 2026Article
- Noninvasive Risk Stratification Based on Renal Tubular Injury Phenotypes: A Deep Learning Study for Predicting Vesicoureteral Reflux in Children.Journal of cellular and molecular medicine · 2026Article
- Application of transformer models in medical image segmentation: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- MMSeg: Multi-scale Vision Mamba for Lightweight Generalizable Medical Image Segmentation.Journal of imaging informatics in medicine · 2026Article
- GlomNet: glomeruli segmentation network for WSI with diverse staining protocols for kidney disease diagnosis.Frontiers in medicine · 2026Article
- Automatic and observable spinal cord compression grading system for lung cancer spinal metastasis.Quantitative imaging in medicine and surgery · 2025Article
- Multi-Scale Fusion for Real-Time Image Observation and Data Analysis of Athletes after Soft Tissue Injury.Current medical imaging · 2025Article
- Technological advances in vitiligo management: perspectives on AI, mobile tools, and clinical utility.Frontiers in medicine · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
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Authors and funding
4 authors.
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Abstract
Medical image segmentation plays a crucial role in assisting clinical diagnosis, yet existing models often struggle with handling diverse and complex medical data, particularly when dealing with multi-scale organ and tissue structures. This paper proposes a novel medical image segmentation model, FE-SwinUper, designed to address these challenges by integrating the strengths of the Swin Transformer and UPerNet architectures. The objective is to enhance multi-scale feature extraction and improve the fusion of hierarchical organ and tissue representations through a feature enhancement Swin Transformer (FE-ST) backbone and an adaptive feature fusion (AFF) module. The FE-ST backbone utilizes self-attention mechanisms to efficiently extract rich spatial and contextual features across different scales, while the AFF module adapts to multi-scale feature fusion, mitigating the loss of contextual information. We evaluate the model on two publicly available medical image segmentation datasets: Synapse multi-organ segmentation dataset and the ACDC cardiac segmentation dataset. Our results show that FE-SwinUper outperforms existing state-of-the-art models in terms of Dice coefficient, pixel accuracy, and Hausdorff distance. The model achieves a Dice score of 91.58% on the Synapse dataset and 90.15% on the ACDC dataset. These results demonstrate the robustness and efficiency of the proposed model, indicating its potential for real-world clinical applications.
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