The DOI matches its Crossref record; 128 of the 128 references it cites were found in a registry or an index.
Deep Learning in Ultrasound Imaging
Open accessAbstract
In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g., sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for the color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have a considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.
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SAN-YOLO for Boiler Weld Defect Detection in Phased-Array Ultrasonic S-Scan Images
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Metadata
- Authors
- van Sloun, R. J. G., Cohen, R., & Eldar, Y. C.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Weizmann Institute of Science (IL)
- Venue
- Proceedings of the IEEE
- Published
- 2020-01
- Volume
- 108
- Issue
- 1
- Pages
- 11-29
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2955895861
- Cited by
- 316
- References
- 127
- Citations by year
- 2026: 25 · 2025: 48 · 2024: 44 · 2023: 43 · 2022: 51 · 2021: 75 · 2020: 26
- FWCI
- 25.78
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings of the IEEE (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- TU/e Research Portal (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- Physical Sciences › Engineering › Biomedical Engineering › Photoacoustic and Ultrasonic Imaging
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Keywords
- deep learning, ultrasonography, adaptive beamforming, color Doppler ultrasound, clutter suppression, super-resolution ultrasound imaging, radiofrequency data, sparsity
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