Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1285-1293.DOI: 10.11772/j.issn.1001-9081.2023040517
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Wei LI1, Ling CHEN2, Xiuyuan XU1, Min ZHU2, Jixiang GUO1, Kai ZHOU1, Hao NIU1, Yuchen ZHANG3, Shanye YI3, Yi ZHANG1, Fengming LUO2()
Received:
2023-05-04
Revised:
2023-07-13
Accepted:
2023-07-14
Online:
2023-08-03
Published:
2024-04-10
Contact:
Fengming LUO
About author:
LI Wei, born in 1994, M. S. candidate. His research interests include medical image segmentation.Supported by:
李威1, 陈玲2, 徐修远1, 朱敏2, 郭际香1, 周凯1, 牛颢1, 张煜宸3, 易珊烨3, 章毅1, 罗凤鸣2()
通讯作者:
罗凤鸣
作者简介:
李威(1994—),男,四川绵竹人,硕士研究生,CCF会员,主要研究方向:医学图像分割基金资助:
CLC Number:
Wei LI, Ling CHEN, Xiuyuan XU, Min ZHU, Jixiang GUO, Kai ZHOU, Hao NIU, Yuchen ZHANG, Shanye YI, Yi ZHANG, Fengming LUO. Interstitial lung disease segmentation algorithm based on multi-task learning[J]. Journal of Computer Applications, 2024, 44(4): 1285-1293.
李威, 陈玲, 徐修远, 朱敏, 郭际香, 周凯, 牛颢, 张煜宸, 易珊烨, 章毅, 罗凤鸣. 基于多任务学习的间质性肺病分割算法[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1285-1293.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023040517
编号 | 模型 | 设置 | DSC |
---|---|---|---|
对照组1 | U-Net | 单任务 | 80.35 |
对照组2 | U-Net | 预训练+微调 | 81.18 |
对照组3 | 多任务分割模型 | 多数据集预训练+微调 | 81.90 |
对照组4 | U-Net | 肺分割过滤输入 | 79.06 |
对照组5 | 多任务分割模型 | 单任务 | 80.26 |
实验组 | 多任务分割模型 | 伪标签+多任务 | 82.61 |
Tab. 1 Comparison of segmentation performance of different groups
编号 | 模型 | 设置 | DSC |
---|---|---|---|
对照组1 | U-Net | 单任务 | 80.35 |
对照组2 | U-Net | 预训练+微调 | 81.18 |
对照组3 | 多任务分割模型 | 多数据集预训练+微调 | 81.90 |
对照组4 | U-Net | 肺分割过滤输入 | 79.06 |
对照组5 | 多任务分割模型 | 单任务 | 80.26 |
实验组 | 多任务分割模型 | 伪标签+多任务 | 82.61 |
主任务权重 | 辅助任务权重 | DSC/% |
---|---|---|
0.95 | 0.05 | 81.42 |
0.90 | 0.10 | 81.95 |
0.80 | 0.20 | 82.15 |
0.70 | 0.30 | 82.12 |
0.60 | 0.40 | 81.56 |
0.50 | 0.50 | 81.11 |
0.40 | 0.60 | 80.77 |
0.20 | 0.80 | 79.53 |
0.05 | 0.95 | 77.80 |
自适应 | 自适应 | 82.61 |
Tab. 2 Comparison experiment results of multi-task loss functions
主任务权重 | 辅助任务权重 | DSC/% |
---|---|---|
0.95 | 0.05 | 81.42 |
0.90 | 0.10 | 81.95 |
0.80 | 0.20 | 82.15 |
0.70 | 0.30 | 82.12 |
0.60 | 0.40 | 81.56 |
0.50 | 0.50 | 81.11 |
0.40 | 0.60 | 80.77 |
0.20 | 0.80 | 79.53 |
0.05 | 0.95 | 77.80 |
自适应 | 自适应 | 82.61 |
模型 | DSC |
---|---|
SegNet[ | 76.42 |
DeepLabv3+[ | 78.95 |
PNet[ | 79.35 |
Swin-Unet[ | 72.41 |
TransUNet[ | 76.67 |
本文模型 | 82.61 |
Tab. 3 Comparison of segmentation performance of different models
模型 | DSC |
---|---|
SegNet[ | 76.42 |
DeepLabv3+[ | 78.95 |
PNet[ | 79.35 |
Swin-Unet[ | 72.41 |
TransUNet[ | 76.67 |
本文模型 | 82.61 |
模型 | DSC | ||
---|---|---|---|
20%样本量 | 50%样本量 | 100%样本量 | |
PNet[ | 75.78 | 78.69 | 79.35 |
本文模型 | 80.04 | 81.24 | 82.61 |
Tab. 4 Comparison of segmentation performance with different sample sizes
模型 | DSC | ||
---|---|---|---|
20%样本量 | 50%样本量 | 100%样本量 | |
PNet[ | 75.78 | 78.69 | 79.35 |
本文模型 | 80.04 | 81.24 | 82.61 |
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