2 天前 - … This study proposes a novel deep-learning model that aims to balance diagnostic
efficacy with patient safety, setting a new benchmark in paediatric diagnostic imaging and …

2 天前 - … learning-based models for medical image translation [16, 17], this work becomes
effective. However, many learning… compact filters for local image feature extraction. These …

2 天前 - … 1 illustrates an IRS-enhanced downlink semantic communication network with an
eavesdropper. In this paper, we consider the semantic image reconstruction task. Since our …

2 天前 - … 15 variants of CNN, LSTM network and tree-based XGBoost predictive models. CNN
and LSTM are built from the notion of neural network, belonging to deep learning’s body of …

2 天前 - … URTNet, a novel unstructured feature fusion network designed for the real-…
Network (SAN) to efficiently merge multi-scale information, minimizing detail loss in feature fusion …

2 天前 - … Additionally, the analysis reveals a growing interest in developing innovative
deep neural networks and multimodal fusion techniques to significantly enhance detection …

2 天前 - … of deep learning techniques in medical image analysis. … images and employed a
residual U-Net to isolate individual vertebrae before reconstructing the complete spinal image, …

2 天前 - … that is utilized by the student network. The student network is trained with the
identity … high-resolution (HR) face images. The performance of the proposed framework is …

2 天前 - … module is designed, which can conduct uniform grid sampling of images to learn
multi-… of images. Then, to reduce the dimension of the feature, we adopt the feature-level fusion …

2 天前 - … In the original Dinov2 ViT network, an input image is first divided into image
patches of size 14× 14. Then, all the patches will be flattened and pass through a linear layer to …