Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs

Pattern Recognition

Volumn 63, Issue , 2017, Pages 476-486

  Tajbakhsh, Nima ^a   Suzuki, Kenji ^a  

^a Illinois Institute of Technology   (United States)

Author keywords

Classification; Computer aided diagnosis; Convolution neural network; Deep learning; Focal lesions; Image based machine learning; Lung nodules; Massive training artificial neural network; Patch based machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOLOGICAL ORGANS; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; COMPUTER VISION; COMPUTERIZED TOMOGRAPHY; CONVOLUTION; DIAGNOSIS; IMAGE ANALYSIS; LEARNING ALGORITHMS; MEDICAL IMAGING; NETWORK ARCHITECTURE; NEURAL NETWORKS; SEMANTICS;

CONVOLUTION NEURAL NETWORK; DEEP LEARNING; FOCAL LESIONS; IMAGE-BASED; LUNG NODULE; PATCH BASED;

LEARNING SYSTEMS;

EID: 84998787346     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.09.029     Document Type: Article

References (47)

1. [1] Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D., Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32:9 (2010), 1627–1645.
2. [2] G. Csurka, C.R. Dance, L. Fan, J. Willamowski, C. Bray, Visual categorization with bags of keypoints, in: Workshop on Statistical Learning in Computer Vision, ECCV, 2004, pp. 1–22.
3. [3] S. Lai, L. Xu, K. Liu, J. Zhao, Recurrent convolutional neural networks for text classification, in: Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
4. [4] Y. Kim, Y. Jernite, D. Sontag, A.M. Rush, Character-aware neural language models, arXiv:1508.06615.
5. [5] B. Hu, Z. Lu, H. Li, Q. Chen, Convolutional neural network architectures for matching natural language sentences, in: Advances in Neural Information Processing Systems, 2014, pp. 2042–2050.
6. [6] T. Unterthiner, A. Mayr, G. Klambauer, S. Hochreiter, Toxicity prediction using deep learning, arXiv:1503.01445.
7. [7] B. Ramsundar, S. Kearnes, P. Riley, D. Webster, D. Konerding, V. Pande, Massively multitask networks for drug discovery, arXiv:1502.02072.
8. [8] I. Wallach, M. Dzamba, A. Heifets, Atomnet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery, arXiv:1510.02855.
9. [9] N. Tajbakhsh, M.B. Gotway, J. Liang, Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks, in: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, 2015.
10. [10] N. Tajbakhsh, S.R. Gurudu, J. Liang, A comprehensive computer-aided polyp detection system for colonoscopy videos, in: International Conference on Information Processing in Medical Imaging, Springer, 2015, pp. 327–338.
11. [11] H.R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, C. Kevin, L. Kim, R.M. Summers, Improving computer-aided detection using convolutional neural networks and random view aggregation, arXiv:1505.03046.
12. [12] H.R. Roth, L. Lu, A. Farag, H.-C. Shin, J. Liu, E. B. Turkbey, R. M. Summers, Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Springer, 2015, pp. 556–564.
13. [13] Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J., Convolutional neural networks for medical image analysis: fine tuning or full training?. IEEE Trans. Med. Imaging 35:5 (2016), 1299–1312.
14. [14] Suzuki, K., Horiba, I., Sugie, N., Efficient approximation of neural filters for removing quantum noise from images. IEEE Trans. Signal Process. 50:7 (2002), 1787–1799.
15. [15] Suzuki, K., Horiba, I., Sugie, N., Nanki, M., Neural filter with selection of input features and its application to image quality improvement of medical image sequences. IEICE Trans. Inf. Syst. 85:10 (2002), 1710–1718.
16. [16] Suzuki, K., Armato, S.G. III, Li, F., Sone, S., Doi, K., Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med. Phys. 30:7 (2003), 1602–1617.
17. [17] Suzuki, K., Li, F., Sone, S., et al. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans. Med. Imaging 24:9 (2005), 1138–1150.
18. [18] Suzuki, K., Yoshida, H., Näppi, J., Dachman, A.H., Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: suppression of rectal tubes. Med. Phys. 33:10 (2006), 3814–3824.
19. [19] Suzuki, K., Yoshida, H., Näppi, J., Armato, S.G. III, Dachman, A.H., Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Med. Phys. 35:2 (2008), 694–703.
20. [20] Suzuki, K., Rockey, D.C., Dachman, A.H., CT colonography:: advanced computer-aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial. Med. Phys. 37:1 (2010), 12–21.
21. [21] Suzuki, K., Zhang, J., Xu, J., Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Trans. Med. Imaging 29:11 (2010), 1907–1917.
22. [22] Xu, J.-W., Suzuki, K., Massive-training support vector regression and gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. Med. Phys. 38:4 (2011), 1888–1902.
23. [23] Fukushima, K., Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36:4 (1980), 193–202.
24. [24] Fukushima, K., Neocognitron capable of incremental learning. Neural Netw. 17:1 (2004), 37–46.
25. [25] Deutsch, S., A simplified version of Kunihiko Fukushima's neocognitron. Biol. Cybern. 42:1 (1981), 17–21.
26. [26] LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., Backpropagation applied to handwritten zip code recognition. Neural Comput. 1:4 (1989), 541–551.
27. [27] B.B. Le Cun, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Handwritten digit recognition with a back-propagation network, in: Advances in Neural Information Processing Systems, Citeseer, 1990.
28. [28] N. Tajbakhsh, S. R. Gurudu, J. Liang, Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks, in: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE, 2015, pp. 79–83.
29. [29] W. Shen, M. Zhou, F. Yang, C. Yang, J. Tian, Multi-scale convolutional neural networks for lung nodule classification, in: S. Ourselin, D.C. Alexander, C.-F. Westin, M.J. Cardoso (Eds.), International Conference on Information Processing in Medical Imaging, Lecture Notes in Computer Science, vol. 9123, Springer International Publishing, 2015, pp. 588–599.
30. [30] H. Roth, L. Lu, A. Seff, K. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey, R. Summers, A new 2.5d representation for lymph node detection using random sets of deep convolutional neural network observations, in: P. Golland, N. Hata, C. Barillot, J. Hornegger, R. Howe (Eds.), International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI 2014, Lecture Notes in Computer Science, vol. 8673, Springer International Publishing, 2014, pp. 520–527.
31. [31] 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 (2015), 214–224.
32. [32] M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, H. Larochelle, Brain tumor segmentation with deep neural networks, arXiv:1505.03540.
33. [33] X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: International Conference on Artificial Intelligence and Statistics, 2010, pp. 249–256.
34. [34] K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: surpassing human-level performance on imagenet classification, arXiv:1502.01852.
35. [35] A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105.
36. [36] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15:1 (2014), 1929–1958.
37. [37] R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2014, pp. 580–587.
38. [38] Sone, S., Takashima, S., Li, F., Yang, Z., Honda, T., Maruyama, Y., Hasegawa, M., Yamanda, T., Kubo, K., Hanamura, K., et al. Mass screening for lung cancer with mobile spiral computed tomography scanner. Lancet 351:9111 (1998), 1242–1245.
39. [39] Li, F., Sone, S., Abe, H., MacMahon, H., Armato, S.G., Doi, K., Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings 1. Radiology 225:3 (2002), 673–683.
40. [40] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: convolutional architecture for fast feature embedding, arXiv:1408.5093.
41. [42] Edwards, D.C., Kupinski, M.A., Metz, C.E., Nishikawa, R.M., Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model. Med. Phys. 29:12 (2002), 2861–2870.
42. [43] Margeta, J., Criminisi, A., Cabrera Lozoya, R., Lee, D.C., Ayache, N., Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition. Comput. Methods Biomech. Biomed. Eng.: Imaging Vis., 2015, 1–11.
43. [44] Y. Bar, I. Diamant, L. Wolf, H. Greenspan, Deep learning with non-medical training used for chest pathology identification, in: SPIE Medical Imaging, International Society for Optics and Photonics, 2015, p. 94140V.
44. [45] Li, Q., Li, F., Shiraishi, J., Katsuragawa, S., Sone, S., Doi, K., Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules. Med. Phys. 30:10 (2003), 2584–2593.
45. [46] M. Kobetski, J. Sullivan, Improved boosting performance by explicit handling of ambiguous positive examples, in: Pattern Recognition Applications and Methods, Advances in Intelligent Systems and Computing, vol. 318, 2015, pp. 17–37.
46. [47] J. Ba, R. Caruana, Do deep nets really need to be deep?, in: Advances in Neural Information Processing Systems, 2014, pp. 2654–2662.
47. [48] Suzuki, K., Doi, K., How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT?. Acad. Radiol. 12:10 (2005), 1333–1341.