SwinMED: Edge-texture enhanced SwinIR for medical image super-resolution
Knowledge-Based Systems, cilt.351, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 351
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.knosys.2026.116665
- Dergi Adı: Knowledge-Based Systems
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, Library, Information Science & Technology Abstracts (LISTA), Information Science & Technology Abstracts (LISTA), Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: Back-projection fine-tuning, Edge-texture enhancement, Hyperparameter optimization, Medical image processing, Prostate MRI, Super-resolution, Swin transformer, SwinIR
- Karadeniz Teknik Üniversitesi Adresli: Evet
Özet
Medical image super-resolution (SR) addresses a fundamental clinical challenge: hardware constraints, acquisition trade-offs, and patient motion frequently produce MRI images with insufficient spatial detail, compromising lesion boundary delineation and the detectability of small pathological findings. Although Transformer-based architectures—and SwinIR in particular—have demonstrated strong SR performance on natural photographic datasets, they do not incorporate explicit mechanisms for enhancing the edge and texture features most relevant to clinical MRI interpretation, creating a domain gap that limits their direct applicability to medical imaging. In this study, we propose SwinMED, a lightweight extension of SwinIR that integrates a learnable Edge-Texture (ET) module (∼110K parameters) targeting multi-scale edge recovery and local texture enhancement through parallel Sobel branches, dilated texture convolutions, gated fusion, and channel attention. The ET module is optimized via a self-supervised back-projection protocol that requires no paired high-resolution reference images, making SwinMED directly applicable in clinical settings. The framework was evaluated on four prostate MRI datasets (KEAH, ProstateX, I2CVB, Prostate158) across T2-weighted, dynamic contrast-enhanced (DCE), and diffusion-weighted (DWI) sequences, under two degradation scenarios and three upscaling factors (×2, ×3, ×4), covering 72 independent experimental conditions. Quantitative evaluation using PSNR, SSIM, MS-SSIM, and LPIPS shows consistent improvements over the baseline SwinIR configuration across all conditions, with average gains of +4.03 dB in PSNR, +0.114 in SSIM, +0.041 in MS-SSIM, and a −0.201 reduction in LPIPS. Positive PSNR and SSIM gains were observed in all 72 conditions; LPIPS improved in 70 out of 72 conditions. These findings indicate that lightweight, domain-targeted edge-texture refinement can complement pretrained transformer SR backbones in medical imaging contexts, providing consistent reconstruction improvements without retraining the full model and without requiring paired high-resolution training data.