MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling

Journal of Digital Imaging

Volumn 31, Issue 4, 2018, Pages 513-519

  Mutasa, Simukayi ^a   Chang, Peter D ^a   Ruzal Shapiro, Carrie ^a   Ayyala, Rama ^a  

^a NewYork Presbyterian Hospital   (United States)

Author keywords

Convolutional neural networks; Deep learning; Endocrinology; Machine learning; Pediatric radiology; Radiology

Indexed keywords

BONE; COMPUTER AIDED DIAGNOSIS; CONVOLUTION; ENDOCRINOLOGY; ERRORS; IMAGE SEGMENTATION; LEARNING SYSTEMS; MEAN SQUARE ERROR; MEDICAL IMAGING; NETWORK ARCHITECTURE; NETWORK LAYERS; NEURAL NETWORKS; PEDIATRICS; PICTURE ARCHIVING AND COMMUNICATION SYSTEMS; RADIATION; RADIOGRAPHY; RADIOLOGY; STATISTICAL TESTS;

ADVANCED ARCHITECTURE; COMPUTER ASSISTED DETECTION; CONVOLUTIONAL NEURAL NETWORK; LEARNING ARCHITECTURES; NEURAL NETWORK ALGORITHM; PEDIATRIC RADIOLOGY; PERFORMANCE METRICES; STATE-OF-THE-ART TECHNIQUES;

DEEP LEARNING;

ADOLESCENT; ARTIFICIAL NEURAL NETWORK; BONE AGE DETERMINATION; CHILD; COHORT ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; FACTUAL DATABASE; FEMALE; HUMAN; INFANT; MACHINE LEARNING; MALE; NEWBORN; PEDIATRICS; PRESCHOOL CHILD; PROCEDURES; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY;

ADOLESCENT; AGE DETERMINATION BY SKELETON; CHILD; CHILD, PRESCHOOL; COHORT STUDIES; DATABASES, FACTUAL; DEEP LEARNING; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; INFANT; INFANT, NEWBORN; MACHINE LEARNING; MALE; NEURAL NETWORKS (COMPUTER); PEDIATRICS; RETROSPECTIVE STUDIES; SENSITIVITY AND SPECIFICITY;

EID: 85045052300     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-018-0053-3     Document Type: Article

References (31)

1. Breen MA et al.: Bone age assessment practices in infants and older children among Society for Pediatric Radiology members. Pediatr Radiol 46(9):1269–1274, 2016
2. Bull RK et al.: Bone age assessment: a large scale comparison of the Greulich and Pyle, and Tanner and Whitehouse (TW2) methods. Arch Dis Child 81(2):172–173, 1999
3. Thodberg HH, Sävendahl L: Validation and reference values of automated bone age determination for four ethnicities. Acad Radiol 17(11):1425–1432, 2010
4. Ontell FK et al.: Bone age in children of diverse ethnicity. AJR. Am J Roentgenol 167(6):1395–1398, 1996
5. Berst MJ et al.: Effect of knowledge of chronologic age on the variability of pediatric bone age determined using the Greulich and Pyle standards. Am J Roentgenol 176(2):507–510, 2001
6. Spampinato C et al.: Deep learning for automated skeletal bone age assessment in X-ray images. Medical image analysis 36:41–51, 2017
7. Thodberg HH et al.: The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28(1):52–66, 2009
8. Lee H, et al: Fully Automated Deep Learning System for Bone Age Assessment. J Digit Imaging (2017): 1–15
9. Shen W, Zhou M, Yang F, Yang C, Tian J: Multi-scale Convolutional Neural Networks for Lung Nodule Classification. Inf Process Med Imaging 24:588–599, 2015
10. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S: Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Trans Med Imaging [Internet]. 2016 May [cited 2017 Jul 2];35(5):1207–16. Available from: http://ieeexplore.ieee.org/document/7422082/
11. Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, et al: Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal [Internet]. Elsevier; 2017 Jan 1 [cited 2017 Sep 18]; 35:303–12. Available from: http://www.sciencedirect.com/science/article/pii/S1361841516301244
12. Dou Q, Chen H, Yu L, Zhao L, Qin J, Wang D, et al: Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks. IEEE Trans Med Imaging [Internet]. 2016 May [cited 2017 Jul 2];35(5):1182–95. Available from: http://ieeexplore.ieee.org/document/7403984/
13. Pereira S, Pinto A, Alves V, Silva CA: Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Trans Med Imaging [Internet]. 2016 May [cited 2017 Jul 2];35(5):1240–51. Available from: http://ieeexplore.ieee.org/document/7426413/
14. Chang PD: Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation. In: Crimi A, Menze B, Maier O, Reyes M, Winzeck S, Handels H, editors. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised [Internet]. Cham: Springer International Publishing; 2016. p. 108–18. Available from: 10.1007/978-3-319-55524-9_11
15. Wang J, Fang Z, Lang N, Yuan H, Su MY, Baldi P: A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput Biol Med 84:137–146, 2017
16. Cao F et al.: Digital hand atlas and web-based bone age assessment: system design and implementation. Comput Med Imaging Graph 24(5):297–307, 2000
17. LeCun Y et al.: Gradient-based learning applied to document recognition. Proceed IEEE 86(11):2278–2324, 1998
18. He K, et al: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016
19. Szegedy C, et al: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. AAAI. 2017
20. Szegedy C, et al: Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. 2015
21. Jaderberg M, Simonyan K, Zisserman A:. Spatial transformer networks. Adv Neural Inf Process Syst 2015
22. Kingma D, Ba J: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 2014
23. Nesterov Y: Gradient methods for minimizing composite objective function. 2007
24. Dozat T: Incorporating nesterov momentum into Adam. 2016
25. Glorot X, Bengio Y: Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. 2010
26. Srivastava N et al.: Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958, 2014
27. Ioffe S, Szegedy C: Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning. 2015
28. Maclaurin D, Duvenaud D, Adams R: Gradient-based hyperparameter optimization through reversible learning. International Conference on Machine Learning. 2015
29. LeCun YA, et al: Efficient backprop. Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012. 9–48
30. Bengio Y: Practical recommendations for gradient-based training of deep architectures. Neural networks: Tricks of the trade. Berlin Heidelberg: Springer, 2012, pp. 437–478
31. Sun C, et al: Revisiting unreasonable effectiveness of data in deep learning era. arXiv preprint arXiv:1707.02968 2017