Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

IEEE Transactions on Medical Imaging

Volumn 35, Issue 5, 2016, Pages 1273-1284

  Van Grinsven, Mark J J P ^a   Van Ginneken, Bram ^a   Hoyng, Carel B ^a   Theelen, Thomas ^a   Sánchez, Clara I ^a  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

Convolutional neural network; deep learning; hemorrhage; selective sampling

Indexed keywords

COMPUTER VISION; CONVOLUTION; ITERATIVE METHODS; LEARNING SYSTEMS; MEDICAL IMAGING; NETWORK ARCHITECTURE; NEURAL NETWORKS; SAMPLING;

COMPUTER VISION APPLICATIONS; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; HEMORRHAGE; HEMORRHAGE DETECTION; RECEIVER OPERATING CHARACTERISTICS; SELECTIVE SAMPLING; STATE OF THE ART;

IMAGE ANALYSIS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CAMERA; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; CONVOLUTIONAL NEURAL NETWORK; DIAGNOSTIC TEST ACCURACY STUDY; FUNDUS CAMERA; HEURISTICS; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; MEASUREMENT ERROR; OPHTHALMOSCOPY; RETINA HEMORRHAGE; SENSITIVITY AND SPECIFICITY; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; EYE FUNDUS; FACTUAL DATABASE; MACHINE LEARNING; PROCEDURES;

DATABASES, FACTUAL; FUNDUS OCULI; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); RETINAL HEMORRHAGE;

EID: 84968665432     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2016.2526689     Document Type: Article

References (48)

1. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
2. J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Netw., vol. 61, pp. 85-117, 2015.
3. K. He, X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification," ArXiv:150201852v1, 2015.
4. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Adv. Neural Inf. Process. Syst., 2012, vol. 25, pp. 1097-1105.
5. D. C. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, "Multi-column deep neural network for traffic sign classification," Neural Netw., vol. 32, pp. 333-338, 2012.
6. D. C. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, "Mitosis detection in breast cancer histology images with deep neural networks," in Proc. MICCAI, 2013, vol. 8150, LNCS, pp. 411-418.
7. Y. Guo, G. Wu, L. A. Commander, S. Szary, V. Jewells, W. Lin, and D. Shent, "Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features," in Proc. MICCAI, 2014, vol. 8674, LNCS, pp. 308-315.
8. A. A. Cruz-Roa, J. E. Arevalo Ovalle, A. Madabhushi, and F. A. González Osorio, "A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection," Proc. MICCAI, vol. 8150, LNCS, pp. 403-410, 2013.
9. R. E. Schapire, "The boosting approach to machine learning: An overview," in Nonlinear Estimation and Classification. New York: Springer, 2003, vol. 171, LNCS, pp. 149-171.
10. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," J. Comput. Syst. Sci., vol. 55, no. 1, pp. 119-139, 1997.
11. H. Chen et al., "Automatic detection of cerebral microbleeds via deep learning based 3d feature representation," in Proc. IEEE Int. Symp. Biomed. Imag., 2015, pp. 764-767.
12. Y. Freund, E. Shamir, and N. Tishby, "Selective sampling using the query by committee algorithm," Mach. Learn., vol. 28, no. 2, pp. 133-168, 1997.
13. M. Lin, K. Tang, and X. Yao, "Dynamic sampling approach to training neural networks for multiclass imbalance classification," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 4, pp. 647-660, Apr. 2013.
14. S. Ertekin, J. Huang, L. Bottou, and L. Giles, "Learning on the border: active learning in imbalanced data classification," in Proc. 16th ACM Conf. Inf. Knowl. Manage., 2007, pp. 127-136.
15. N. Cheung, P. Mitchell, and T. Y. Wong, "Diabetic retinopathy," Lancet, vol. 376, no. 9735, pp. 124-136, 2010.
16. M. D. Abràmoff, M. K. Garvin, and M. Sonka, "Retinal imaging and image analysis," IEEE Rev. Biomed. Eng., vol. 3, pp. 169-208, 2010.
17. P. Jitpakdee, P. Aimmanee, and B. Uyyanonvara, "A survey on hemorrhage detection in diabetic retinopathy retinal images," in Proc. 9th Int. Conf. Elect. Eng./Electron., Comput., Telecommun. Inf. Technol., 2012, pp. 1-4.
18. G. B. Kande, T. S. Savithri, and P. V. Subbaiah, "Automatic detection of microaneurysms and hemorrhages in digital fundus images," J. Digit. Imag., vol. 23, no. 4, pp. 430-437, 2010.
19. G. Quellec et al., "Optimal wavelet transform for the detection of microaneurysms in retina photographs," IEEE Trans. Med. Imag., vol. 27, no. 9, pp. 1230-1241, Sep. 2008.
20. U. M. Akram and S. A. Khan, "Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy," J. Med. Syst., vol. 36, no. 5, pp. 3151-3162, 2012.
21. M. Niemeijer, B. van Ginneken, J. Staal, M. S. A. Suttorp-Schulten, and M. D. Abràmoff, "Automatic detection of red lesions in digital color fundus photographs," IEEE Trans. Med. Imag., vol. 24, no. 5, pp. 584-592, May 2005.
22. B. Antal and A. Hajdu, "Improving microaneurysm detection in color fundus images by using context-aware approaches," Comput. Med. Imag. Graph., vol. 37, no. 5, pp. 403-408, 2013.
23. C. Sinthanayothin et al., "Automated detection of diabetic retinopathy on digital fundus images," Diabetic Med., vol. 19, no. 2, pp. 105-112, 2002.
24. S. Deepa and S. Vijayprasath, "Certain investigation of the retinal hemorrhage detection in fundus images," Int. J. Electron. Commun. Eng., vol. 2, no. 2, pp. 29-40, 2015.
25. L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abràmoff, "Splat feature classification with application to retinal hemorrhage detection in fundus images," IEEE Trans. Med. Imag., vol. 32, no. 2, pp. 364-375, Feb. 2013.
26. D. Ballard, "Generalizing the hough transform to detect arbitrary shapes," Pattern Recognit., vol. 13, no. 2, pp. 111-122, 1981.
27. A. G. Marrugo and M. S. Millán, "Retinal image analysis: Preprocessing and feature extraction," in J. Phys., Conf. Ser., 2011, vol. 274, p. 012039.
28. S. H. Rasta, M. E. Partovi, H. Seyedarabi, and A. Javadzadeh, "A comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement," J. Med. Signals Sensors, vol. 5, no. 1, pp. 40-48, 2015.
29. B. Graham, Kaggle diabetic retinopathy detection competition report Univ. Warwick, Tech. Rep., 2015.
30. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," ArXiv:14091556, 2014.
31. A. Seff et al., "2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers," Proc. MICCAI, vol. 8673, LNCS, pp. 544-552, 2014.
32. P. Efraimidis and P. Spirakis, "Weighted random sampling," in Encyclopedia of Algorithms. New York: Springer, 2008, pp. 1-99.
33. V. Rajan, R. K. Ghosh, and P. Gupta, "An efficient parallel algorithm for random sampling," Inform. Process. Lett., vol. 30, no. 5, pp. 265-268, 1989.
34. A. Lipowski and D. Lipowska, "Roulette-wheel selection via stochastic acceptance," Physica A, Stat. Mechan. Appl., vol. 391, no. 6, pp. 2193-2196, 2012.
35. X. He and E. Frey, "ROC, LROC, FROC, AFROC: An alphabet soup," J. Am. Coll. Radiol., vol. 6, no. 9, pp. 652-655, 2009.
36. M. J. J. P. van Grinsven et al., "Automatic drusen quantification and risk assessment of age-related macular degeneration on color fundus images," Invest. Ophthalmol. Vis. Sci., vol. 54, no. 4, pp. 3019-3027, 2013.
37. N. Karssemeijer et al., "Computer-aided detection versus independent double reading of masses on mammograms," Radiology, vol. 227, no. 1, pp. 192-200, 2003.
38. A. Gubern-Mérida et al., "Automated localization of breast cancer in DCE-MRI," Med. Image Anal., vol. 20, no. 1, pp. 265-274, 2015.
39. F. Samuelson and N. Petrick, "Comparing image detection algorithms using resampling," in Proc. IEEE Int. Symp. Biomed. Imag., 2006, pp. 1312-1315.
40. F. Samuelson, N. Petrick, and S. Paquerault, "Advantages and examples of resampling for CAD evaluation," in Proc. IEEE Int. Symp. Biomed. Imag., 2007, pp. 492-495.
41. J. Bergstra and Y. Bengio, "Random search for hyper-parameter optimization," J. Mach. Learn. Res., vol. 13, no. 1, pp. 281-305, 2012.
42. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, 2014.
43. S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," ArXiv:150203167, 2015.
44. C. Farabet, C. Couprie, L. Najman, and Y. LeCun, "Learning hierarchical features for scene labeling," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1915-1929, Aug. 2013.
45. N. V. Chawla, N. Japkowicz, and A. Kotcz, "Editorial: Special issue on learning from imbalanced data sets," SIGKDD Explorat., vol. 6, pp. 1-6, 2004.
46. H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Trans. Knowl. Data Eng., vol. 21, no. 9, pp. 1263-1284, Sep. 2009.
47. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," ArXiv:14114038, 2015.
48. A. Halevy, P. Norvig, and F. Pereira, "The unreasonable effectiveness of data," IEEE Intell. Syst., vol. 24, no. 2, pp. 8-12, Mar./Apr. 2009.