The RSNA Pediatric Bone Age Machine Learning Challenge
Author:
Funder
National Institute for Health Research
Publisher
Radiological Society of North America (RSNA)
Subject
Radiology Nuclear Medicine and imaging
Link
http://pubs.rsna.org/doi/pdf/10.1148/radiol.2018180736
Reference7 articles.
1. Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs
2. Greulich WW, Pyle SI. Radiographic atlas of skeletal development of the hand and wrist. Stanford, Calif: Stanford University Press, 1999.
3. Fully Automated Deep Learning System for Bone Age Assessment
4. Computerized Bone Age Estimation Using Deep Learning Based Program: Evaluation of the Accuracy and Efficiency
5. MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling
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