电子科技 ›› 2024, Vol. 37 ›› Issue (1): 17-23.doi: 10.16180/j.cnki.issn1007-7820.2024.01.003

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基于元学习和神经架构搜索的半监督医学图像分割方法

于智洪,李菲菲   

  1. 上海理工大学 光电信息与计算机工程学院,上海 200093
  • 收稿日期:2022-08-24 出版日期:2024-01-15 发布日期:2024-01-11
  • 作者简介:于智洪(1997-),女,硕士研究生。研究方向:图像处理与模式识别。|李菲菲(1970-),女,博士,教授。研究方向:多媒体信息处理、图像处理与模式识别、信息检索等。
  • 基金资助:
    上海市高校特聘教授(东方学者)岗位计划(ES2015XX)

Semi-Supervised Medical Image Segmentation Method Based on Meta-Learning and Neural Architecture Search

YU Zhihong,LI Feifei   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology, Shanghai 200093,China
  • Received:2022-08-24 Online:2024-01-15 Published:2024-01-11
  • Supported by:
    The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning(ES2015XX)

摘要:

多数医学图像分割方法主要在相同或者相似医疗数据领域进行训练和评估,意味其需要大量像素级别的标注。但这些模型在领域分布外的数据集上面临挑战,被称为“域偏移”问题。通常使用固定的U形分割架构解决该问题,导致其无法更好地适应特定分割任务。文中提出了一种基于梯度的元学习与神经架构搜索方法,可以根据特定任务调整分割网络以实现良好的性能并且拥有良好的泛化能力。该方法主要使用特定任务进行架构搜索模块来进一步提升分割效果,再使用基于梯度的元学习训练算法提升泛化能力。在公共数据集M&Ms上,在5%标签数据下,其Dice和Hausdorff distance分别为79.62%、15.38%。在2%标签数据下,其Dice和Hausdorff distance分别为74.03%、17.05%。与其他主流方法相比,文中所提方法拥有更好的泛化能力。

关键词: 医学图像分割, 元学习, 神经架构搜索, 域泛化, 解耦表示, 半监督学习, 卷积神经网络, 深度学习

Abstract:

Most medical image segmentation methods mainly focus on training and evaluating in the same or similar medical data domain, which need lots of pixel-level annotations. However, these models face challenges in out-of-distribution medical data set, which is known as "domain shift" problem. A fixed U-shaped segmentation structure is usually used to solve this problem, resulting in it not being better adapted to specific partition tasks.A gradient-based meta-learning and neural architecture search method is proposed in this study, which can adjust the segmentation network according to specific tasks to achieve good performance and have good generalization ability. This method mainly uses the specific task to carry out the architecture search module to further improve the segmentation effect, and then uses the gradient-based meta-learning training algorithm to improve the generalization ability.On the public dataset M&Ms, under the 5% label data, its Dice and Hausdorff distance are 79.62% and 15.38%. Under 2% label data, its Dice and Hausdorff distance are 74.03% and 17.05%.Compared with other mainstream methods, the proposed method has better generalization ability.

Key words: medical image segmentation, meta-learning, neural architecture search, domain generalization, disentangle representations, semi-supervised learning, convolutional neural network, deep learning

中图分类号: 

  • TP391
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