3D Dense Separated Convolution Module for Volumetric Medical Image Analysis

Applied Sciences

Liang Zou¹  Lei Qu²  Changfeng Wu² 

[1] Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada;School of Electronics and Information Engineering, Anhui University, Hefei 236601, China;

关键词: convolutional neural networks;    biomedical imaging;    image segmentation;    medical diagnosis;   

DOI  :  10.3390/app10020485

来源: DOAJ

【 摘 要 】

With the thriving of deep learning, 3D convolutional neural networks have become a popular choice in volumetric image analysis due to their impressive 3D context mining ability. However, the 3D convolutional kernels will introduce a significant increase in the amount of trainable parameters. Considering the training data are often limited in biomedical tasks, a trade-off has to be made between model size and its representational power. To address this concern, in this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module to replace the original 3D convolutional kernels. The 3D-DSC module is constructed by a series of densely connected 1D filters. The decomposition of 3D kernel into 1D filters reduces the risk of overfitting by removing the redundancy of 3D kernels in a topologically constrained manner, while providing the infrastructure for deepening the network. By further introducing nonlinear layers and dense connections between 1D filters, the network’s representational power can be significantly improved while maintaining a compact architecture. We demonstrate the superiority of 3D-DSC on volumetric medical image classification and segmentation, which are two challenging tasks often encountered in biomedical image computing.

【 授权许可】

Unknown