Self-Supervised Representation Learning for Ultrasound Video
Open accessAbstract
Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications. Therefore, there is significant interest in learning representations from unlabelled raw data. In this paper, we propose a self-supervised learning approach to learn meaningful and transferable representations from medical imaging video without any type of human annotation. We assume that in order to learn such a representation, the model should identify anatomical structures from the unlabelled data. Therefore we force the model to address anatomy-aware tasks with free supervision from the data itself. Specifically, the model is designed to correct the order of a reshuffled video clip and at the same time predict the geometric transformation applied to the video clip. Experiments on fetal ultrasound video show that the proposed approach can effectively learn meaningful and strong representations, which transfer well to downstream tasks like standard plane detection and saliency prediction.
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Nine of the innate relationship studies designed their pre-text task based on different image transformations, including rotation prediction, horizontal flip prediction, reordering shuffled slices, and patch order prediction.
“Nine of these studies designed their innate relationship pre-text task based on different image transformations, including rotation prediction44–47, horizontal flip prediction48, reordering shuffled slices49, and patch order prediction46,50–52.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [49]
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Cites
Squeeze-and-Excitation Networks
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Squeeze-and-Excitation Networks
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What Do Different Evaluation Metrics Tell Us About Saliency Models?
Bylinskii, Z., Judd, T., Oliva, A., Torralba, A. B., & Durand, F. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2815601 · Cited by 835 · see citing works
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., & Rueckert, D. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2712367 · Cited by 416 · see citing works
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Self-citationUltrasound Image Representation Learning by Modeling Sonographer Visual Attention
Droste, R., Cai, Y., Sharma, H., Chatelain, P., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_46 · Cited by 31 · see citing works
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Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
Taleb, A., Kirchler, M., Monti, R., & Lippert, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02024 · Cited by 83 · see citing works
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Horgan, R., Nehme, L., & Abuhamad, A. (2023) · Prenatal Diagnosis · doi:10.1002/pd.6411 · Cited by 72 · see citing works
“…studies assessed the ability of deep learning models to undergo self ‐ supervised learning by correcting the order of a reshuffled fetal video clip, 115 by context restoration of unlabeled 2D fetal images 116 and the use of random forests classifiers for classification of unlabeled fetal…”
BackgroundDrukker, L., Sharma, H., Droste, R., Alsharid, M., Chatelain, P., Noble, J. A., & Papageorghiou, A. T. (2021) · Scientific Reports · doi:10.1038/s41598-021-92829-1 · Cited by 59 · see citing works
Self-citationSelf-Supervised Contrastive Video-Speech Representation Learning for Ultrasound
Jiao, J., Cai, Y., Alsharid, M., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59716-0_51 · Cited by 38 · see citing works
Self-citationCornelissen, S., van der Putten, J. A., Boers, T. G. W., Jukema, J. B., Fockens, K. N., Bergman, J. J. G. H. M., van der Sommen, F., & de With, P. H. N. (2021) · 2021 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip42928.2021.9506121 · Cited by 2 · see citing works
- Authors
- Jiao, J., Droste, R., Drukker, L., Papageorghiou, A. T., & Noble, J. A.
- Affiliations
- University of Oxford (GB)
- Venue
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2020-04
- Volume
- 2020
- Pages
- 1847-1850
- PMC
- PMC7266673
- OpenAlex
- W3027445961
- Cited by
- 56
- References
- 10
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 17 · 2022: 11 · 2021: 7 · 2020: 4
- FWCI
- 3.67
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/M013774/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- self-supervised representation learning, ultrasound video, prenatal ultrasound, saliency prediction, transfer learning, annotation-free learning
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