Deep Convolutional Neural Networks for Endotracheal Tube Position and X-ray Image Classification: Challenges and Opportunities

Journal of Digital Imaging

Volumn 30, Issue 4, 2017, Pages 460-468

  Lakhani, Paras ^a  

^a THOMAS JEFFERSON UNIVERSITY HOSPITAL   (United States)

Author keywords

Artificial intelligence; Artificial neural networks (ANNs); Classification; Machine learning; Radiography

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTERIZED TOMOGRAPHY; CONVOLUTION; IMAGE CLASSIFICATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; RADIOGRAPHY; TUBES (COMPONENTS); X RAY RADIOGRAPHY;

AREA UNDER THE CURVES; CONFIDENCE INTERVAL; CONVOLUTIONAL NEURAL NETWORK; DATA AUGMENTATION; NONPARAMETRIC APPROACHES; RECEIVER OPERATING CHARACTERISTICS; STATISTICAL DIFFERENCES; X-RAY IMAGE CLASSIFICATIONS;

DEEP NEURAL NETWORKS;

ABDOMINAL RADIOGRAPHY; AREA UNDER THE CURVE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DEVICES; ENDOTRACHEAL INTUBATION; HUMAN; INFORMATION PROCESSING; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; THORAX RADIOGRAPHY;

AREA UNDER CURVE; DATASETS AS TOPIC; HUMANS; INTUBATION, INTRATRACHEAL; NEURAL NETWORKS (COMPUTER); RADIOGRAPHY, ABDOMINAL; RADIOGRAPHY, THORACIC; ROC CURVE;

EID: 85020637979     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-017-9980-7     Document Type: Article

References (23)

1. Goodman LR, Conrardy PA, Laing F, Singer MM: Radiographic evaluation of endotracheal tube position. Am J Roentgenol 127(3):433–4,1976
2. Brunel W, Coleman DL, Schwartz DE, Peper E, Cohen NH: Assessment of routine chest roentgenograms and the physical examination to confirm endotracheal tube position. Chest 96(5):1043–5,1989
3. Zwillich CW, Pierson DJ, Creagh CE, Sutton FD, Schatz E, Petty TL: Complications of assisted ventilation: a prospective study of 354 consecutive episodes. Am J Med 57(2):161–70,1974
4. Ramakrishna B, Brown M, Goldin J, Cagnon C, Enzmann D: An improved automatic computer aided tube detection and labeling system on chest radiographs. Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150R (February 23, 2012); doi:10.1117/12.911839
5. Kao EF, Jaw TS, Li CW, Chou MC, Liu GC: Automated detection of endotracheal tubes in paediatric chest radiographs. Comput Methods Prog Biomed 118(1):1–10,2015
6. Chen S, Zhang M, Yao L, Xu W: Endotracheal tubes positioning detection in adult portable chest radiography for intensive care unit. Int J Comput Assist Radiol Surg 11(11):2049–57,2016
7. Krizhevsky A, Sutskever I, Hinton GE: Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 1097–1105, 2012
8. Szegedy C, Liu W, Jia Y, et al.: Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 1–9, 2015
9. Russakovsky O, Deng J, Su H, et al.: Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252,2015
10. Bar Y, Diamant I, Wolf L, Greenspan H: Deep learning with non-medical training used for chest pathology identification. Proc.SPIE Medical Imaging, International Society for Optics and Photonics: 9414, 2015
11. Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J: High-throughput classification of radiographs using deep convolutional neural networks. J Digit Imaging 11:1–7, 2016
12. Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108:214–224, 2015
13. Shin HC, Roth HR, Gao M, et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298,2016
14. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–10,2016
15. Cho J, Lee K, Shin E, Choy G, Do S: How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv preprint arXiv:1511.06348, 2015
16. Jia Y, Shelhamer E, Donahue J, et al.: Caffe: convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia 2014. ACM, 2014
17. nd ed. Berlin: Springer, 2009, pp. 219–257
18. Steyerberg EW, Vickers AJ, Cook NR et al.: Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology 21(1):128–138,2010
19. Bradley AP: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159,1997
20. Fawcett T.: ROC graphs: notes and practical considerations for researchers. Mach Learn 31(1):1–38,2004
21. Delrue L, Gosselin R, Ilsen B, Van Landeghem A, de Mey J, Duyck P: Difficulties in the interpretation of chest radiography. In Comparative Interpretation of CT and Standard Radiography of the Chest. Berlin Heidelberg: Springer, 2011, pp. 27–49
22. Yosinski J, Clune J, Bengio Y, Lipson H: How transferable are features in deep neural networks? In Advances in neural information processing systems, 2014, pp. 3320–3328
23. Simonyan K, Vedaldi A, Zisserman A: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034, 2013