Semi-supervised learning of fetal anatomy from ultrasound
Tan J, Au A, Meng Q, Kainz B (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer
Book Volume: 11795 LNCS
Pages Range: 157-164
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030333904
DOI: 10.1007/978-3-030-33391-1_18
Abstract
Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images. Anatomy classification in fetal 2D ultrasound is an ideal problem setting to test whether these results translate to non-ideal data. Our results indicate that inclusion of a challenging background class can be detrimental and that semi-supervised learning mostly benefits classes that are already distinct, sometimes at the expense of more similar classes.
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United Kingdom (GB)How to cite
APA:
Tan, J., Au, A., Meng, Q., & Kainz, B. (2019). Semi-supervised learning of fetal anatomy from ultrasound. In Qian Wang, Fausto Milletari, Nicola Rieke, Hien V. Nguyen, Badri Roysam, Shadi Albarqouni, M. Jorge Cardoso, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 157-164). Shenzhen, CHN: Springer.
MLA:
Tan, Jeremy, et al. "Semi-supervised learning of fetal anatomy from ultrasound." Proceedings of the 1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Qian Wang, Fausto Milletari, Nicola Rieke, Hien V. Nguyen, Badri Roysam, Shadi Albarqouni, M. Jorge Cardoso, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le, Springer, 2019. 157-164.
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