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Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks
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“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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Metadata
- 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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