The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
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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Cites
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2014) · Defense Technical Information Center (DTIC) · doi:10.21236/ada623249 · Cited by 1,101
Graves, A. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-24797-2_2 · Cited by 579
Gradient-based learning algorithms for recurrent networks and their computational complexity
Williams, R. J., & Zipser, D. (1995) · Cited by 413
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
Chen, H., Ni, D., Qin, J., Li, S., Yang, X., Wang, T., & Heng, P. A. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2425041 · Cited by 379
Self-citationStandard Plane Localization in Ultrasound by Radial Component Model and Selective Search
Ni, D., Yang, X., Chen, X., Chin, C.-T., Chen, S., Heng, P. A., Li, S., Qin, J., & Wang, T. (2014) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2014.06.006 · Cited by 65
Self-citationIntegration of Local and Global Features for Anatomical Object Detection in Ultrasound
Rahmatullah, B., Papageorghiou, A. T., & Noble, J. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_50 · Cited by 55
Localizing target structures in ultrasound video – A phantom study
Kwitt, R., Vasconcelos, N. M., Razzaque, S., & Aylward, S. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.05.003 · Cited by 39
Searching for Structures of Interest in an Ultrasound Video Sequence
Maraci, M. A., Napolitano, R., Papageorghiou, A. T., & Noble, J. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10581-9_17 · Cited by 33
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Attention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014
Gland segmentation in colon histology images: The glas challenge contest
Sirinukunwattana, K., Pluim, J. P. W., Chen, H., Qi, X., Heng, P., Guo, Y. B., Wang, L. Y., Matuszewski, B. J., Bruni, E., Sanchez, U., et al. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.08.008 · Cited by 1,014
Self-citationDeep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798
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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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