Automated Bone Age Assessment Using Deep Learning with Attention Module
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50327-6_23
Reference15 articles.
1. Ayala-Raggi, S.E. et al.: A supervised incremental learning technique for automatic recognition of the skeletal maturity, or can a machine learn to assess bone age without radiological training from experts?’ Int. J. Patt. Recogn. Artific. Intell., 32(01):1 860 002 (2018)
2. Booz, C., et al.: Evaluation of a computer-aided diagnosis system for auto- mated bone age assessment in comparison to the greulich-pyle atlas method: a multireader study. J. Comput. Assisted Tomogr. 43(1), 39–45 (2019)
3. Ren, X., et al.: Regression convolutional neural network for automated pediatric bone age assessment from hand radiograph. IEEE J. Biomed. Health Inf. 23(5), 2030–2038 (2018)
4. Digital Hand Atlas Dataset, https://ipilab.usc.edu/research/baaweb /. Accessed: 2023-02-01 (cit. on pp. 4, 7, 14, 25, 27)
5. Spampinato, C., Palazzo, S., Giordano, D., Aldinucci, M., Leonardi, R.: Deep learning for automated skeletal bone age assessment in x-ray images. Med. Image Analysis 36, 41–51 (2017)
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