Pulmonary Nodule Classification with 3D Convolutional Neural Networks
Resumo
Lung cancer is a leading cause of death worldwide and its early detection is critical for patient survival. However, the diagnosis is still a challenging task, in which computeraided diagnosis (CADx) systems try to assist by providing a second opinion to a radiologist. In this work, we propose a 3D Convolutional Neural Network for classification of solid pulmonary nodules into benign and malignant. We evaluated different approaches for the nodule volume assembling and tuned our models in an automated fashion. Our models achieved satisfactory results, with AUC of 0.89, accuracy of 81.37% and a sensibility of 84.83%. Moreover, our results have shown that the first slices of a nodule provide the best results and only five nodule slices are enough for a 3D CNN achieve its best results.
Palavras-chave:
Lung nodule classification, 3D convolutional
neural networks
Referências
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J. Bergstra, D. Yamins, and D. D. Cox, Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms, in Proceedings of the 12th Python in science conference. Citeseer, 2013, pp. 13 - 20.
F. Chollet et al., Keras, https://github.com/fchollet/keras, 2015.
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S. G. Armato, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman et al., The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans, Medical physics, vol. 38, no. 2, pp. 915 - 931, 2011. DOI: 10.1118/1.3469350
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A. Lima Filho, A. P. Machado, and M. Oliveira, Modelo para Classificacção de Nódulos Pulmonares Pequenos usando Descritores Radiomics, Master's thesis, University of Alagoas - (UFAL), 2016.
C. I. S. Silva, E. Marchiori, A. S. Souza Júnior, and N. L. Müller, Illustrated brazilian consensus of terms and fundamental patterns in chest ct scans, Jornal Brasileiro de Pneumologia, vol. 36, no. 1, pp. 99 - 123, 2010.
J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Ke ´gl, Algorithms for hyper-parameter optimization, in Advances in neural information processing systems, 2011, pp. 2546 - 2554.
S. Blandin Knight, P. A. Crosbie, H. Balata, J. Chudziak, T. Hussell, and C. Dive, Progress and prospects of early detection in lung cancer, Open biology, vol. 7, no. 9, p. 170070, 2017. DOI: 10.1098/rsob.170070
K.-L. Hua, C.-H. Hsu, S. C. Hidayati, W.-H. Cheng, and Y. -J. Chen, Computer-aided classification of lung nodules on computed tomography images via deep learning technique, OncoTargets and therapy, vol. 8, 2015.
G. Kang, K. Liu, B. Hou, and N. Zhang, DOI: 3d multi-view convolutional neural networks for lung nodule classification, PloS one, vol. 12, no. 11, p. e0188290, 2017. 10.1371/journal.pone.0188290
M. J. Chuquicusma, S. Hussein, J. Burt, and U. Bagci, How to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis, in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018, pp. 240 - 244.
J. R. Ferreira, M. C. Oliveira, and P. M. de Azevedo-Marques, Characterization of pulmonary nodules based on features of margin sharpness and texture, Journal of digital imaging, vol. 31, no. 4, pp. 451 - 463, 2018. DOI: 10.1007/s10278-017-0029-8
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. Sa ´nchez, A survey on deep learning in medical image analysis, Medical image analysis, vol. 42, pp. 60 - 88, 2017. DOI: 10.1016/j.media.2017.07.005
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1 - 9.
S. Ren, K. He, R. Girshick, and J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, in Advances in neural information processing systems, 2015, pp. 91 - 99. DOI: 10.1109/tpami.2016.2577031
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, Semantic image segmentation with deep convolutional nets and fully connected crfs, arXiv preprint arXiv:1412.7062
D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, and G.- Z. Yang, Deep learning for health informatics, IEEE journal of biomedical and health informatics, vol. 21, no. 1, pp. 4 - 21, 2017. DOI: 10.1109/jbhi.2016.2636665
G. Montavon, G. Orr, and K.-R. Mu¨ller, Neural networks: tricks of the trade. springer, 2012, vol. 7700.
A. Coates and A. Y. Ng, The importance of encoding versus training with sparse coding and vector quantization, in Proceedings of the 28th international conference on machine learning (ICML-11), 2011, pp. 921 - 928.
N. Pinto, D. Doukhan, J. J. DiCarlo, and D. D. Cox, A high-throughput screening approach to discovering good forms of biologically inspired visual representation, PLoS computational biology, vol. 5, no. 11, p. e1000579, 2009.
J. Bergstra and Y. Bengio, Random search for hyper-parameter optimization, Journal of Machine Learning Research, vol. 13, no. Feb, pp. 281 - 305, 2012.
A. Tartar, A. Akan, and N. Kilic, A novel approach to malignantbenign classification of pulmonary nodules by using ensemble learning classifiers, in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2014, pp. 4651 - DOI: 10.1109/embc.2014.6944661
F. Han, H. Wang, G. Zhang, H. Han, B. Song, L. Li, W. Moore, H. Lu, H. Zhao, and Z. Liang, Texture feature analysis for computer-aided diagnosis on pulmonary nodules, Journal of digital imaging, vol. 28, no. 1, pp. 99 - 115, DOI: 10.1007/s10278-014-9718-8
A. Felix, M. C. Oliveira, A. Machado, and J. R. Ferreira, Using 3D Texture and Margin Sharpness Features on Classification of Small Pulmonary Nodules, in Proceedings..., Conference on Graphics, Patterns and Images, (SIBGRAPI). IEEE Computer Society´s Conference Publishing Services, 2016. DOI: 10.1109/sibgrapi.2016.061
Y. Yang, X. Feng, W. Chi, Z. Li, W. Duan, H. Liu, W. Liang, W. Wang, P. Chen, J. He et al., Deep learning aided decision support for pulmonary nodules diagnosing: a review, Journal of thoracic disease, vol. 10, no. Suppl 7, p. S867, 2018.
W. Shen, M. Zhou, F. Yang, C. Yang, and J. Tian, Multi-scale convolutional neural networks for lung nodule classification, in International Conference on Information Processing in Medical Imaging. Springer, 2015, pp. 588 - 599.
R. Dey, Z. Lu, and Y. Hong, Diagnostic classification of lung nodules using 3d neural networks, in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018, pp. 774 - 778. DOI: 10.1109/isbi.2018.8363687
J. J. F. O. M. C. Lima, Lucas Lins de, Efficient hyperparameter optimization of convolutional neural networks on classification of early pulmonary nodules, in 32nd IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2019), 2019.
M. Pumperla. ( 2019) Hyperas. [Online]. Available: http://maxpumperla.com/hyperas/.
J. Bergstra, D. Yamins, and D. D. Cox, Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms, in Proceedings of the 12th Python in science conference. Citeseer, 2013, pp. 13 - 20.
F. Chollet et al., Keras, https://github.com/fchollet/keras, 2015.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., Tensorflow: A system for largescale machine learning, in 12th fUSENIXg Symposium on Operating Systems Design and Implementation (fOSDIg 16), 2016, pp. 265 - 283.
S. G. Armato, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman et al., The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans, Medical physics, vol. 38, no. 2, pp. 915 - 931, 2011. DOI: 10.1118/1.3469350
J. R. F. Junior, M. C. Oliveira, and P. M. de Azevedo-Marques, Cloudbased nosql open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research, Journal of digital imaging, vol. 29, no. 6, pp. 716 - 729, 2016.
A. Lima Filho, A. P. Machado, and M. Oliveira, Modelo para Classificacção de Nódulos Pulmonares Pequenos usando Descritores Radiomics, Master's thesis, University of Alagoas - (UFAL), 2016.
C. I. S. Silva, E. Marchiori, A. S. Souza Júnior, and N. L. Müller, Illustrated brazilian consensus of terms and fundamental patterns in chest ct scans, Jornal Brasileiro de Pneumologia, vol. 36, no. 1, pp. 99 - 123, 2010.
J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Ke ´gl, Algorithms for hyper-parameter optimization, in Advances in neural information processing systems, 2011, pp. 2546 - 2554.
Publicado
09/09/2019
Como Citar
JATOBÁ, Anthony E. A.; LIMA, Lucas L.; OLIVEIRA, Marcelo C..
Pulmonary Nodule Classification with 3D Convolutional Neural Networks. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 15. , 2019, São Bernardo do Campo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 67-72.
DOI: https://doi.org/10.5753/wvc.2019.7630.
