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MT-ONet: Mixed Transformer O-Net for Medical Image Segmentation
编号：66 访问权限：公开 更新：2022-12-22 01:12:12 浏览：307次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

演示文件
摘要
In the past few years, the deep learning is widely used in the medical industry due to its advantage. Constructed using Convolutional Neural Networks (CNN), the U-Net framework has become the industry standard for solving medical image segmentation tasks. Nonetheless, this framework is incapable of entirely learning all global and remote semantic information. It has been demonstrated that the transformer structure collects more global information than U-Net but less local information than CNN. To improve the performance of segmentation and classification in medical images while maximizing global and local data, we integrate O-Net with Mixed Transformer [1], this fuses the advantages of CNN and Transformer. This enables us to maximize both types of data. We combine CNN, Mixed Transformer, and Local-Global Gaussian-Weighted Self- Attention (LGG-SA) in the encoder component of our proposed O-Net architecture to obtain more global and local background information. The decoder part combines the Mixed Transformer and CNN blocks to obtain the results. The segmentation capability of the proposed network is evaluated by the multi-organ CT dataset containing synaptic information. The results of our trials demonstrate that the proposed MT-ONet can deliver superior segmentation performance relative to cutting-edge methods, resulting in improved classification precision.
 
关键词
convolutional neural network (CNN);O-net;deep learning;self-attention;medical image segmentation
报告人
Pengfei Zheng
University of California, Santa Barbara

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重要日期

  • 会议日期

    11月30日

    2022

    至

    12月02日

    2022

  • 11月30日 2022

    初稿截稿日期

  • 12月24日 2022

    报告提交截止日期

  • 04月13日 2023

    注册截止日期

主办单位
Harbin Insititute of Technology
China Instrument and Control Society
Heilongjiang Instrument and Control Society
Chinese Institute of Electronics
IEEE I&M Society Harbin Chapter
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