He, S, Lin, Z, Yang, X et al. (13 more authors) (2021) Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound. In: Lian, C, Cao, X, Rekik, I, Xu, X and Yan, P, (eds.) Machine Learning in Medical Imaging. 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, 27 Sep - 01 Oct 2021, Strasbourg, France. Springer , pp. 190-198. ISBN 978-3-030-87588-6
Abstract
Standard plane recognition plays an important role in prenatal ultrasound (US) screening. Automatically recognizing the standard plane along with the corresponding anatomical structures in US image can not only facilitate US image interpretation but also improve diagnostic efficiency. In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously. Our contribution is three-fold. First, we represent the class correlation by word embeddings to capture the fine-grained semantic and latent statistical concurrency. Second, we equip the MLL with a graph convolutional network to explore the inner and outer relationship among categories. Third, we propose a novel cluster relabel-based contrastive learning algorithm to encourage the divergence among ambiguous classes. Extensive validation was performed on our large in-house dataset. Our approach reports the highest accuracy as 90.25% for standard planes labeling, 85.59% for planes and structures labeling and mAP as 94.63%. The proposed MLL scheme provides a novel perspective for standard plane recognition and can be easily extended to other medical image classification tasks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2021. This is an author produced version of a conference paper published in Machine Learning in Medical Imaging (Lecture Notes in Computer Science, vol 12966). Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Sep 2021 09:49 |
Last Modified: | 07 Dec 2021 02:21 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-87589-3_20 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177883 |