Central to CSAN-UNet's design is the Cascaded Channel and Spatial Convolutional Attention Module (CSCAM), a novel component that adaptively enhances multi-level features and mitigates the loss of target information attributable to downsampling processes. Additionally, the Channel-priorit...
Boosting power line inspection in bad weather: Removing weather noise with channel-spatial attention-based UNet doi:10.1007/s11042-023-17554-5Power line inspectionBad weatherImage derainingImage desnowingImage dehazingDatasetPower line inspection based on UAVs can effectively improve the inspection ...
Rectal tumor segmentationResidual convolutionAttention mechanismUNetMultimedia Tools and Applications - The precise segmentation of rectal tumors is a key step in the diagnosis and treatment of rectal cancer. This paper aims to study the automatic segmentation task......
UNetsaliency modelspatial attentiondeep learningCancers are getting pretty common these days and in that the second most common cancer in the world after lung cancer is breast cancer. The primary screening techniques for early diagnosis of cancerous nodules in women breast are Ultrasound, Mammography,...
Regarding network design, integrating a UNet based on an encoder–decoder architecture with a spatial–spectral attention network (SSA-Net) based on residual spatial–spectral attention (Res-SSA) blocks further enhances the ability to extract spatial and spectral features. The experiment shows ...
Within UNet, we introduce the SSA-Net, which utilizes spatial–spectral attention mechanisms, between the layers of the encoder and their corresponding decoder counterparts. This design aims to enhance the expression capability of both spatial and spectral features. Additionally, we propose novel ...