Detection of anatomical landmarks in medical images plays a crucial role in understanding anatomy and facilitating automated processing. In recent years, various deep neural network methods have been developed for automated landmark detection. However, deep neural networks suffer from limitations in expressive power and are prone to overfitting. In this study, we propose a novel approach that combines the multi-head attention mechanism with U-Net architecture to enhance the expressive capability of deep learning. The proposed model consists of encoding and decoding modules. The encoding module leverages a dual multi-head attention mechanism to learn local features, while the decoding module employs a depth-wise separable convolutional sequence corresponding to the encoding module. These two modules are concatenated through skip connections. We evaluate our model on an open-source dataset of lateral skull x-ray images, which includes 400 images with 19 landmark points in each image. Notably, experimental results demonstrate that our proposed model outperforms known open-source models in terms of performance, providing evidence for the effectiveness of our proposed approach.
Diffusion strategies play a crucial role in distributed estimation, and with technological advancements, many effective diffusion algorithms resistant to impulsive noise have been proposed, with Diffusion maximum correntropy criterion being one of them. On the other hand, diffusion strategies require each node to share information with neighboring nodes, which introduces a certain communication overhead. To reduce communication costs, this paper leverages the interference caused by impulsive noise to design two communication mechanisms, where nodes execute corresponding communication strategies based on their individual states. Experimental results demonstrate that this algorithm effectively reduces communication overhead while maintaining performance as much as possible.
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