ConvMedSegNet: A multi-receptive field depthwise convolutional neural network for medical image segmentation.

In order to achieve highly precise medical image segmentation, this paper presents ConvMedSegNet, a novel convolutional neural network designed with a U-shaped architecture that seamlessly integrates two crucial modules: the multi-receptive field depthwise convolution module (MRDC) and the guided fusion module (GF). The MRDC module's primary function is to capture texture information of varying sizes through multi-scale convolutional layers. This information is subsequently utilized to enhance the correlation of global feature data by expanding the network's width. This strategy adeptly preserves the inherent inductive biases of convolution while concurrently amplifying the network's ability to establish dependencies on global information. Conversely, the GF module assumes responsibility for implementing multi-scale feature fusion by connecting the encoder and decoder components. It facilitates the transfer of information between features that are separated over substantial distances through guided fusion, effectively minimizing the loss of critical data. In experiments conducted on public medical image datasets such as BUSI and ISIC2018, ConvMedSegNet outperforms several advanced competing methods, yielding superior results. Additionally, the code can be accessed at https://github.com/csust-yixin/ConvMedSegNet.

A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification.

Aiming at arrhythmia heartbeats classification, a novel multifeature fusion deep learning-based method is proposed. The stationary wavelet transforms (SWT) and RR interval features are firstly extracted. Based on the traditional one-dimensional convolutional neural network (1D-CNN), a parallel multibranch convolutional network is designed for training. The subband of SWT is input into the multiscale 1D-CNN separately. The output fused with RR interval features are fed to the fully connected layer for classification. To achieve the lightweight network while maintaining the powerful inference capability of the multibranch structure, the redundant branches of the network are removed by reparameterization. Experimental results and analysis show that it outperforms existing methods by many in arrhythmic heartbeat classification.