TL;DR
A novel Swin Transformer–based U-Net architecture whose primary contribution lies in a Swin-Enhanced Cross Attention (SECA)-driven decoding strategy, rather than the use of a Swin encoder alone, is proposed.
A Multi-scale Residual Gated Attention U-Net (MRGA-UNet) for liver tumor segmentation, which achieves superior segmentation performance primarily in DSC and VOE, while maintaining competitive performance on other metrics.
Early and accurate analysis of breast cancer is critical for improving patient outcomes. Ultrasound imaging is widely used for breast tumor screening due to its safety, accessibility, and low cost. We propose DGTN (Diffused Graph-Transformer Network), a lightweight, end-to-end deep learning model that jointly performs...
A lightweight Vision Transformer UNet is proposed that combines the hierarchical feature extraction capability of UNet with the global context modeling of Vision Transformers, enabling effective learning of both local and global features while maintaining computational efficiency with only 2.6 million trainable paramet...
Introduction Benefiting from its radiation-free property and excellent soft-tissue contrast, magnetic resonance imaging (MRI) has become a crucial modality for prostate cancer diagnosis. However, automatic segmentation of prostate cancer lesions in MRI is still a challenging task due to the considerable variations in l...
Prostate cancer, a leading cause of cancer-related deaths among men globally, necessitates the development of precise diagnostic and treatment strategies. Accurate segmentation of prostate cancer in medical imaging, particularly in MRI scans, is crucial for early diagnosis and clinical decisions. Traditional manual seg...
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