Semi-supervised information fusion for medical image analysis: Recent progress and future perspectives

Weng, Ying; Zhang, Yiming; Wang, Wenxin; Dening, Tom

Abstract

Supervised machine learning requires training on the dataset with annotation. However, fine-grained annotation is very expensive to acquire. In the medical image analysis domain, the sheer volume of data and lack of annotation limit the performance of the model. To address these limitations, semi-supervised information fusion has recently emerged as an important and promising paradigm owing to its ability to exploit labelled and unlabelled data and combine information from multiple sources to obtain a more robust and accurate performance. In this survey, we review the recent progress of semi-supervised information fusion for medical image analysis. Moreover, we categorize the state-of-the-art information fusion applications of semi-supervised learning with in-depth analysis. Finally, we discuss the challenges and outline the future perspective.

Citation

Weng, Y., Zhang, Y., Wang, W., & Dening, T. (2024). Semi-supervised information fusion for medical image analysis: Recent progress and future perspectives. Information Fusion, 106, Article 102263. https://doi.org/10.1016/j.inffus.2024.102263

• Journal Article Type: Article
• Acceptance Date: Jan 18, 2024
• Online Publication Date: Jan 22, 2024
• Publication Date: 2024-06
• Deposit Date: Feb 18, 2024
• Publicly Available Date: Feb 20, 2024
• Journal: Information Fusion
• Print ISSN: 1566-2535
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 106
• Article Number: 102263
• DOI: https://doi.org/10.1016/j.inffus.2024.102263
• Public URL: https://nottingham-repository.worktribe.com/output/31452852
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1566253524000411?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: Semi-supervised information fusion for medical image analysis: Recent progress and future perspectives; Journal Title: Information Fusion; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.inffus.2024.102263; Content Type: article; Copyright: © 2024 The Authors. Published by Elsevier B.V.