Dynamic Learning Convolutional Network with Skip Layers for Image Segmentation

Training deeper networks make an active research topic, but the appropriate is the best for specific tasks. In this paper, we proposed to design a good and stable convolutional neural network to solve many image segmentation tasks. We designed an optional skip connection module, which contains three convolutional holes. We use different sizes of convolution kernels to obtain different size of views. This module connects up sampling and down sampling layers of the same size, and dynamically adjust the parameters of the model through the skip gate unit on each convolution kernel. Our experiments on retinal and lung segmentation tasks to confirm the usability of the module, and the model automatically selects the appropriate convolutional layer and achieves a specific segmentation effect after training. The experiments prove the feasibility of the scheme and provide help for further optimization of the segmentation model with a suitable convolutional layers and parameters.