分割
任务(项目管理)
人工智能
计算机科学
监督学习
编码器
半监督学习
注释
深度学习
机器学习
模式识别(心理学)
人工神经网络
工程类
操作系统
系统工程
作者
Wenxia Wu,Jing Yan,Yuanshen Zhao,Qiuchang Sun,Huailing Zhang,Jingliang Cheng,Dong Liang,Yinsheng Chen,Zhenyu Zhang,Zhicheng Li
出处
期刊:Displays
[Elsevier]
日期:2023-03-06
卷期号:78: 102402-102402
被引量:11
标识
DOI:10.1016/j.displa.2023.102402
摘要
Accurate survival prediction is essential for precision oncology in patients with glioma. However, current deep learning-based survival analysis methods highly rely on segmented tumor regions, which requires tedious manual annotation. Semi-supervised segmentation offers an efficient way to reduce the annotation burden. However, most studies consider survival prediction and semi-supervised segmentation as two separated problems. Here, we proposed a multi-task learning approach for concurrent survival prediction and semi-supervised tumor segmentation. We train a shared multi-modal Transformer encoder to extract features from multiple modalities and fuse them at different levels. The extracted features are employed to construct contrast learning loss and survival analysis loss to implement semi-supervised segmentation and survival analysis, respectively. Experiments are conducted on two datasets from two local hospitals. Our method achieves comparable or slightly better results than state-of-the-art semi-supervised segmentation methods and achieves acceptable survival analysis results. Our data suggests that the proposed multi-task architecture can enhance both segmentation and survival prediction tasks in a semi-supervised learning manner.
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