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

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

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


Authors

Ying Weng

Yiming Zhang

Wenxin Wang

Profile image of TOM DENING

TOM DENING TOM.DENING@NOTTINGHAM.AC.UK
Clinical Professor in Dementia Research



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
Electronic ISSN 1872-6305
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.

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