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O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification by Tao Wang; Junlin Lan; Zixin Han; Ziwei Hu; Yuxiu Huang; Yanglin Deng; Hejun Zhang; Jianchao Wang; Musheng Chen; Haiyan Jiang; Ren-Guey Lee; Qinquan Gao; Ming Du; Tong Tong; Gang Chen is a Neuroscience article available to read on EtoBox.

What is O-Net: A Novel Framework With Deep Fusion of CNN and Transformer for Simultaneous Segmentation and Classification about?

The application of deep learning in the medical field has continuously made huge breakthroughs in recent years. Based on convolutional neural network (CNN), the U-Net framework has become the benchmark of the medical image segmentation task. However, this framework cannot fully learn global information and remote semantic information. The transformer structure has been demonstrated to capture global information relatively better than the U-Net, but the ability to learn local information is not as good as CNN. Therefore, we propose a novel network referred to as the O-Net, which combines the advantages of CNN and transformer to fully use both the global and the local information for improving medical image segmentation and classification. In the encoder part of our proposed O-Net framework, we combine the CNN and the Swin Transformer to acquire both global and local contextual features. In the decoder part, the results of the Swin Transformer and the CNN blocks are fused to get the final results. We have evaluated the proposed network on the synapse multi-organ CT dataset and the ISIC 2017 challenge dataset for the segmentation task. The classification network is simultaneously trai

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Author

Tao Wang; Junlin Lan; Zixin Han; Ziwei Hu; Yuxiu Huang; Yanglin Deng; Hejun Zhang; Jianchao Wang; Musheng Chen; Haiyan Jiang; Ren-Guey Lee; Qinquan Gao; Ming Du; Tong Tong; Gang Chen

Publisher

Frontiers Media SA

Published

2022

Language

EN

Field

Neuroscience (Life Sciences)