U-Net with Dense Encoder, Residual Decoder and Depth-wise Skip Connections
- Resource Type
- Conference
- Authors
- Ying, Weiqin; Li, Junhui; Wu, Yu; Zheng, Kaijie; Deng, Yali; Li, Jiachen
- Source
- 2020 International Joint Conference on Neural Networks (IJCNN) Neural Networks (IJCNN), 2020 International Joint Conference on. :1-6 Jul, 2020
- Subject
- Bioengineering
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Convolution
Image segmentation
Decoding
Medical diagnostic imaging
Task analysis
Feature extraction
convolutional neural network
medical image segmentation
U-Net
convolution block
skip connection
- Language
- ISSN
- 2161-4407
For tasks like medical image segmentation and understanding, U-Net is one of the most prominent convolutional neural networks (CNNs) in recent years. Most of the models for image segmentation today are the variants of the classical U-Net. By applying some improvements on the convolution blocks and skip connections in U-Net, this paper proposes a dense-residual depth-wise U-Net (DR-DW U-Net). The DR-DW U-Net aims at extracting more useful features and alleviating the pressure during gradient descent. It adopts dense blocks in the left encoding path while using residual blocks in the right decoding path. In addition, the skip connections of DR-DW U-Net are injected with additional convolution blocks in the form of depth-wise convolution. The experimental results on two medical segmentation datasets indicate that the DR-DW U-Net achieves competitive performances and notable improvements over the classical U-Net as well as some well-designed variants of U-Net.