Content-Adaptive U-Net Architecture for Medical Image Segmentation

In this paper, we introduce a modification of the popular U-Net neural network architecture for medical image segmentation. Our proposed architecture replaces the concatenation operations in the traditional U-Net's skip connections with content-adaptive convolution, thereby significantly reducing the number of parameters of the network. Our experiments on two segmentation tasks - cell nuclei segmentation, and pneumo-thorax segmentation - demonstrated that the modified architecture achieves higher segmentation accuracy compared to the original U-net.