皮肤癌病变组织分割的多通道并行U型网络

高钟宇; 禹龙; 田生伟; 吴卫东; 张德志

doi:10.13568/j.cnki.651094.651316.2021.12.10.0003

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皮肤癌病变组织分割的多通道并行U型网络

信息科学与技术 | 更新时间：2026-01-21

- 皮肤癌病变组织分割的多通道并行U型网络
- 皮肤癌病变组织分割的多通道并行U型网络
- 新疆大学学报（自然科学版中英文） 2022年39卷第6期页码：707-719
- 作者机构：
  
  1. 新疆大学软件学院
  2. 新疆大学网络中心
  3. 新疆维吾尔自治区人民医院
- 作者简介：
- 基金信息：
  
  新疆维吾尔自治区重点研究与发展项目（2021B03001-4）
- DOI：10.13568/j.cnki.651094.651316.2021.12.10.0003
  中图分类号： TP391.41;R739.5
- 纸质出版：2022
- 稿件说明：
移动端阅览
[1]高钟宇,禹龙,田生伟,等.皮肤癌病变组织分割的多通道并行U型网络[J].新疆大学学报(自然科学版)(中英文),2022,39(06):707-719.
[1]高钟宇,禹龙,田生伟,等.皮肤癌病变组织分割的多通道并行U型网络[J].新疆大学学报(自然科学版)(中英文),2022,39(06):707-719. DOI： 10.13568/j.cnki.651094.651316.2021.12.10.0003.

DOI：10.13568/j.cnki.651094.651316.2021.12.10.0003.

摘要

医学图像分割已经成为辅助诊断当中重要的一环．受困于单通道模型特征提取能力的限制，网络所能获取的信息总量有限，导致性能无法进一步提升．针对信息数量不足的问题，提出了一种多通道模型．与单通道模型相比，多通道模型提供了更多互补的特征信息，有助于更好地进行特征提取与数据表达．结果如下：（1）设计了动态卷积发散模块（DSC BM），用于构建多通道模型．（2）设计了动态卷积集束模块（DSC AM），用于融合多尺度特征．（3）使用动态卷积发散模块与动态卷积集束模块构建多通道并行U型网络（MCPU-Net）．所提出的方法在国际公开数据集ISIC2017进行训练和评估，MCPU-Net的总体Acc指标为0.933

JI指标为0.772.

Abstract

Medical image segmentation has become an important part of assisted diagnosis. Trapped by the limitations of the single channel model feature extraction capability

the total amount of information that the network can acquire is limited

resulting in no further performance improvement. In response to the insufficient amount of information

this paper proposes a multi-channel model for processing unimodal data. Compared with the single-channel model

the multi-channel model brings more complementary feature information

which helps better data representation and feature extraction. The results are as follows:(1) The dynamic selective kernel module branch model(DSC BM) is designed to construct a multi-channel model.(2) The dynamic selective kernel module aggregation module(DSC AM) is designed for fusing multi-scale features.(3) A multi-channel parallel U-shape network(MCPU-Net) is constructed. The overall Acc metric of MCPU-Net was 0.933

and the J I metric was 0.772 by training and evaluation in the international public dataset ISIC2017.

关键词

Keywords

references

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