Multi-scale informationAccurate pancreas segmentation is essential for the diagnosis of pancreas disease, while it is still challenging due to the variable structure and small size of the pancreas. In this paper, we propose a Multi-scale Deformable U-Net with Cos-spatial and Channel Hybrid ...
提出了一种基于U-Net结构的具有密集特征融合的多尺度增强去雾网络。 该方法基于增强反馈和误差反馈两种原理进行了设计,并证明了该方法适用于脱雾问题。 通过在该模型的解码器中加入增强-操作-减弱(SOS)的增强策略,开发了一个简单而有效的增强解码器来逐步恢复无雾图像。 为了解决在U-Net架构中保留空间信息的问题,...
该方法是基于两种原理设计的——boosting 和 error feedback,表明它们适用于去雾问题。通过在所提出的模型的解码器中加入“Strengthen-Operate-Subtract”增强策略,本文开发了一种简单有效的增强解码器来逐步恢复无雾图像。为了解决在U-Net架构中保留空间信息的问题,本文使用back-projection反馈方案设计了一个特征密集的融...
called URoadNet, effectively encodes fine-grained local road connectivity and holistic global topological semantics while decoding multiscale road network information. URoadNet offers a novel alternative to the U-Net architecture by integrating connectivity attention, which can exploit intra-road interactions...
In this work, we propose an end to end trainable U-net-based deep learning method to accelerate MRI acquisition process. To enable more efficient feature extraction, a multi-kernel convolutional feature fusion mechanism is introduced in each encoder and decoder stage of U-net. Multi-kernel ...
Multi-scale dense connections, which means containing shorter connections between layers close to the input and output, also makes much deeper U-net possible. We adopt the optimal model based on the experiment and propose a novel Multi-scale Dense U-Net (MDU-Net) architecture with quantization....
A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental...
Aiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the ...
Firstly, we introduce a multi-scale squeeze and excitation module into U-Net network. The module can suppress irrelevant regions, extract image features from multiple angles, and highlight segmentation tasks. Then, adding residual structure to standard convolution layer can effectively avoid gradient ...
The judgment of gear failure is based on the pitting area ratio of gear. Traditional gear pitting calculation method mainly rely on manual visual inspection. This method is greatly affected by human factors, and is greatly affected by the working experie