Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks
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Transfer learning in CNN representation can be effective in various cross-modality imaging settings, including RGB to depth images, natural images to general CT and MRI images, natural images to neuroimaging, and natural images to ultrasound data.
“Finally, transfer learning in CNN representation, as empirically verified in previous literature [59]–[61], [11], [62] can be effective in various cross-modality imaging settings (RGB images to depth images [59], [60], natural images to general CT and MRI images [11], and natural images to neuroimaging [61] or ultrasound [62] data).”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [62]
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- Authors
- Chen, H., Dou, Q., Ni, D., Cheng, J.-Z., Qin, J., Li, S., & Heng, P.-A.
- Affiliations
- Chinese University of Hong Kong (HK)
- Shenzhen University (CN)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 507-514
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2294318823
- Cited by
- 180
- References
- 11
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 12 · 2023: 14 · 2022: 17 · 2021: 17 · 2020: 20 · 2019: 29 · 2018: 19 · 2017: 20 · 2016: 18
- FWCI
- 7.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- prenatal ultrasound, knowledge transfer, recurrent neural networks, spatiotemporal feature learning, multi-task learning, limited training data, fetal anatomy, temporal sequence learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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