AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A dual U-shaped network based on convolutional neural network and vision transformer is designed to sufficiently achieve the cross-modal feature fusion of image and text to compensate for the defects of existing datasets.
Experimental results show that TDU-Net outperforms other methods in both segmentation accuracy and generalization, and significantly improving clinical diagnosis efficiency and accuracy.
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
Accurate medical image segmentation plays a vital role in clinical diagnostics by facilitating the precise delineation of anatomical structures and pathological regions. However, the performance of existing segmentation methods is often constrained by the scarcity of high-quality annotated datasets, as manual labeling...
Medical images provide essential information for diagnosing and monitoring various diseases and systemic disorders. With advancements in deep learning and neural networks, numerous methods have been proposed to achieve high-level medical image segmentation results. However, the variability of tiny structures and their...
Medical image segmentation aims to accurately delineate organs, tissues, or lesion regions from complex medical images. However, existing hybrid models based on Transformers and convolutional neural networks still suffer from limitations in local detail modeling and cross-layer feature fusion, which often leads to blur...
Medical image segmentation requires computational methods that accurately capture global context, local boundaries, and multiscale anatomical structures while remaining reproducible across different applications. This article presents a protocol for constructing, training, and evaluating a Multi-view Vision Mamba U-Sha...
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