Semi-supervised Unpaired Medical Image Segmentation Through Task-affinity Consistency

Jingkun Chen, Jianguo Zhang, Kurt Debattista, Jungong Han

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
166 Downloads (Pure)

Abstract

Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing the cost of manual annotation of clinicians by using unlabelled data, when developing medical image segmentation tools. However, to date, most existing semi-supervised learning (SSL) algorithms treat the labelled images and unlabelled images separately and ignore the explicit connection between them; this disregards essential shared information and thus hinders further performance improvements. To mine the shared information between the labelled and unlabelled images, we introduce a class-specific representation extraction approach, in which a task-affinity module is specifically designed for representation extraction. We further cast the representation into two different views of feature maps; one is focusing on low-level context, while the other concentrates on structural information. The two views of feature maps are incorporated into the task-affinity module, which then extracts the class-specific representations to aid the knowledge transfer from the labelled images to the unlabelled images. In particular, a task-affinity consistency loss between the labelled images and unlabelled images based on the multi-scale class-specific representations is formulated, leading to a significant performance improvement. Experimental results on three datasets show that our method consistently outperforms existing state-of-the-art methods. Our findings highlight the potential of consistency between class-specific knowledge for semi-supervised medical image segmentation. The code and models are to be made publicly available at https://github.com/jingkunchen/TAC.
Original languageEnglish
Pages (from-to)594-605
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume42
Issue number3
Early online date11 Oct 2022
DOIs
Publication statusPublished - 01 Mar 2023

Keywords

  • Consistency
  • Contextual
  • Feature extraction
  • Image segmentation
  • Knowledge transfer
  • Medical diagnostic imaging
  • Segmentation
  • Semi-supervised
  • Semisupervised learning
  • Structural
  • Task analysis
  • Task-affinity
  • Training
  • contextual
  • task-affinity
  • consistency
  • structural
  • segmentation
  • Algorithms
  • Supervised Machine Learning

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