AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
Accurate computed tomography (CT)-based segmentation of kidneys and renal tumors provides quantitative anatomical information that can support downstream volumetric analysis and treatment-planning workflows. This study presents FAU-Net, a Feature-Aggregated Attention U-Net that combines Cross-Channel Attention (CCA), M...
Dense State Space Selection U-Net, a dense state space selection network, is proposed, to enhance liver tumor segmentation from CT images, by integrating a gating mechanism and dense state space blocks, resulting in superior segmentation accuracy.
Accurate kidney and renal-tumor segmentation is challenging because lesion size, location, morphology, and boundary contrast vary substantially across abdominal CT scans. Most existing methods rely on a single form of local evidence and struggle to recover the boundaries of small lesions while maintaining global anat...
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.
Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and disease monitoring. However, developing automated segmentation models that generalize across diverse tumor characteristics, imaging protocols, acquisition sites, and patient pop...
Liver tumor segmentation is a significant task in clinical imaging that involves detecting liver tumors and distinguishing them from the surrounding liver tissue in CT scans. Precision segmentation performs important roles in the initial detection of liver cancer, treatment planning, and monitoring disease development,...
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