Research progress of computer-aided diagnosis in cancer based on deep learning and medical imaging_Journal of Biomedical Engineering

Authors：

CHEN Shihui ¹ , LIU Weixiang ¹ , QIN Jing ² , CHEN Liangliang ¹ , BIN Guo ¹ , ZHOU Yuxiang ¹ ,  WANG Tianfu ¹ , HUANG Bingsheng ¹

1. School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China;
2. School of Nursing, The Hong Kong Polytechnic University, Hong Kong, P.R.China;

Corresponding author：

WANG Tianfu, Email: tfwang@szu.edu.cn

Keywords：

cancer; medical images; deep learning; computer-aided diagnosis; tumor segmentation; tumor classification

DOI：

10.7507/1001-5515.201609047

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The dramatically increasing high-resolution medical images provide a great deal of useful information for cancer diagnosis, and play an essential role in assisting radiologists by offering more objective decisions. In order to utilize the information accurately and efficiently, researchers are focusing on computer-aided diagnosis (CAD) in cancer imaging. In recent years, deep learning as a state-of-the-art machine learning technique has contributed to a great progress in this field. This review covers the reports about deep learning based CAD systems in cancer imaging. We found that deep learning has outperformed conventional machine learning techniques in both tumor segmentation and classification, and that the technique may bring about a breakthrough in CAD of cancer with great prospect in the future clinical practice.

Citation： CHEN Shihui, LIU Weixiang, QIN Jing, CHEN Liangliang, BIN Guo, ZHOU Yuxiang, WANG Tianfu, HUANG Bingsheng. Research progress of computer-aided diagnosis in cancer based on deep learning and medical imaging. Journal of Biomedical Engineering, 2017, 34(2): 314-319. doi: 10.7507/1001-5515.201609047 Copy

1.	Siegel R L, Miller K D, Jemal A. Cancer statistics, 2015. CA Cancer J Clin, 2015, 65(1): 5-29.
2.	CA: 2015中国癌症统计数据发布. 中国医学创新, 2016(5): 6.
3.	石平, 雷增杰, 蓝宗富, 等. 如何快速有效地获取网上病理学图像资源. 西北医学教育, 2007, 15(2): 219-221.
4.	Doi K. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 2007, 31(4/5): 198-211.
5.	Xu Yan, Jia Zhipeng, Ai Yuqing, et al. Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation//2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brisbane: IEEE, 2015: 947-951.
6.	Cheng J Z, Ni D, Chou Y H, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6: 24454.
7.	孙志军, 薛磊, 许阳明, 等. 深度学习研究综述. 计算机应用研究, 2012, 29(8): 2806-2810.
8.	Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
9.	Guo Yanming, Liu Yu, Oerlemans A, et al. Deep learning for visual understanding: A review. Neurocomputing, 2016, 187(SI): 27-48.
10.	Hinton G E, Sejnowski T J. Learning and relearning in Boltzmann machines. Cambridge, MA: MIT Press, 1986.
11.	Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
12.	雷成, 叶小勇, 李小波. 深度学习技术及其在肿瘤分类中的应用. 智能计算机与应用, 2014, 4(6): 17-19.
13.	LISA lab, University of Montreal. Deep learning tutorial release 0.1[EB/OL]. (2015-09-01)[2016-09-20]. http://deeplearning.net/ tutorial/contents.html.
14.	罗蔓, 黄靖, 杨丰. 基于多模态3D-CNNs特征提取的MRI脑肿瘤分割方法. 科学技术与工程, 2014, 14(31): 78-83.
15.	Dhungel N, Carneiro G, Bradley A P. Deep structured learning for mass segmentation from mammograms//IEEE International Conference on Image Processing (ICIP). Québec: IEEE, 2015: 2950-2954.
16.	Dhungel N, Carneiro G, Bradley A P. Deep learning and structured prediction for the segmentation of mass in mammograms//Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference. Munich: Springer, 2015, 9349(0302-9743): 605-612.
17.	Cha K H, Hadjiiski L M, Samala R K, et al. Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets//SPIE Medical Imaging. California: International Society for Optics and Photonics, 2016, 9785: 978512-978517.
18.	Su Hai, Liu Fujun, Xie Yuanpu, et al. Region segmentation in histopathological breast cancer images using deep convolutional neural network//2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). New York: IEEE, 2015: 55-58.
19.	Nowozin S, Lampert C H. Structured learning and prediction in computer vision. Foundations and Trends® in Computer Graphics and Vision, 2011, 6(3/4): 185-365.
20.	Cho J, Lee K, Shin E, et al. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy[EB/OL]. (2016-01-07)[2016-09-20]. https://arxiv. org/abs/1511.06348.
21.	Carneiro G, Nascimento J, Bradley A P. Unregistered multiview mammogram analysis with pre-trained deep learning models//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference. Munich: Springer, 2015, 9351(0302-9743): 652-660.
22.	Hua K L, Hsu C H, Hidayati S C, et al. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther, 2015, 8: 2015-2022.
23.	Shen W, Zhou M, Yang F, et al. Multi-scale Convolutional Neural Networks for lung nodule classification//Information Processing in Medical Imaging. Isle of Skye: Springer, 2015, 9123(0302-9743): 588-599.
24.	Xu Jun, Xiang Lei, Hang Renlong, et al. Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology//2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Beijing: IEEE, 2014: 999-1002.
25.	Xu Jun, Xiang Lei, Liu Qingshan, et al. Stacked Sparse Autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging, 2016, 35(1): 119-130.
26.	Arevalo J, González F A, Ramos-Pollán R, et al. Convolutional neural networks for mammography mass lesion classi-fication//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milano: IEEE, 2015: 797-800.
27.	Arevalo J, González F A, Ramos-Pollán R, et al. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed, 2016, 127: 248-257.
28.	Fotin S V, Yin Y, Haldankar H, et al. Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches//SPIE Medical Imaging. California: International Society for Optics and Photonics, 2016, 9785: 97850X-97856.
29.	Wu Kaizhi, Chen Xi, Ding Mingyue. Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Optik (Stuttg), 2014, 125(15): 4057-4063.
30.	Liu Xiao, Shi Jun, Zhang Qi. Tumor classification by deep polynomial network and multiple kernel learning on small ultrasound image dataset//Machine Learning in Medical Imaging. Munich: Springer, 2015, 9352(0302-9743): 313-320.
31.	Shi Jun, Zhou Shichong, Liu Xiao, et al. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing, 2016, 194: 87-94.
32.	Abdel-Zaher A M, Eldeib A M. Breast cancer classification using deep belief networks. Expert Syst Appl, 2016, 46: 139-144.
33.	Masood A, Al-Jumaily A, Anam K. Self-supervised learning model for skin cancer diagnosis//Neural Engineering (NER) 2015: 7th International IEEE/EMBS Conference. Montpellier: IEEE, 2015: 1012-1015.
34.	Codella N, Cai J, Abedini M, et al. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images//Machine Learning in Medical Imaging. Munich: Springer, 2015, 9352(0302-9743): 118-126.

1. Siegel R L, Miller K D, Jemal A. Cancer statistics, 2015. CA Cancer J Clin, 2015, 65(1): 5-29.
2. CA: 2015中国癌症统计数据发布. 中国医学创新, 2016(5): 6.
3. 石平, 雷增杰, 蓝宗富, 等. 如何快速有效地获取网上病理学图像资源. 西北医学教育, 2007, 15(2): 219-221.
4. Doi K. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 2007, 31(4/5): 198-211.
5. Xu Yan, Jia Zhipeng, Ai Yuqing, et al. Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation//2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brisbane: IEEE, 2015: 947-951.
6. Cheng J Z, Ni D, Chou Y H, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6: 24454.
7. 孙志军, 薛磊, 许阳明, 等. 深度学习研究综述. 计算机应用研究, 2012, 29(8): 2806-2810.
8. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
9. Guo Yanming, Liu Yu, Oerlemans A, et al. Deep learning for visual understanding: A review. Neurocomputing, 2016, 187(SI): 27-48.
10. Hinton G E, Sejnowski T J. Learning and relearning in Boltzmann machines. Cambridge, MA: MIT Press, 1986.
11. Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
12. 雷成, 叶小勇, 李小波. 深度学习技术及其在肿瘤分类中的应用. 智能计算机与应用, 2014, 4(6): 17-19.
13. LISA lab, University of Montreal. Deep learning tutorial release 0.1[EB/OL]. (2015-09-01)[2016-09-20]. http://deeplearning.net/ tutorial/contents.html.
14. 罗蔓, 黄靖, 杨丰. 基于多模态3D-CNNs特征提取的MRI脑肿瘤分割方法. 科学技术与工程, 2014, 14(31): 78-83.
15. Dhungel N, Carneiro G, Bradley A P. Deep structured learning for mass segmentation from mammograms//IEEE International Conference on Image Processing (ICIP). Québec: IEEE, 2015: 2950-2954.
16. Dhungel N, Carneiro G, Bradley A P. Deep learning and structured prediction for the segmentation of mass in mammograms//Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference. Munich: Springer, 2015, 9349(0302-9743): 605-612.
17. Cha K H, Hadjiiski L M, Samala R K, et al. Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets//SPIE Medical Imaging. California: International Society for Optics and Photonics, 2016, 9785: 978512-978517.
18. Su Hai, Liu Fujun, Xie Yuanpu, et al. Region segmentation in histopathological breast cancer images using deep convolutional neural network//2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). New York: IEEE, 2015: 55-58.
19. Nowozin S, Lampert C H. Structured learning and prediction in computer vision. Foundations and Trends® in Computer Graphics and Vision, 2011, 6(3/4): 185-365.
20. Cho J, Lee K, Shin E, et al. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy[EB/OL]. (2016-01-07)[2016-09-20]. https://arxiv. org/abs/1511.06348.
21. Carneiro G, Nascimento J, Bradley A P. Unregistered multiview mammogram analysis with pre-trained deep learning models//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference. Munich: Springer, 2015, 9351(0302-9743): 652-660.
22. Hua K L, Hsu C H, Hidayati S C, et al. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther, 2015, 8: 2015-2022.
23. Shen W, Zhou M, Yang F, et al. Multi-scale Convolutional Neural Networks for lung nodule classification//Information Processing in Medical Imaging. Isle of Skye: Springer, 2015, 9123(0302-9743): 588-599.
24. Xu Jun, Xiang Lei, Hang Renlong, et al. Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology//2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Beijing: IEEE, 2014: 999-1002.
25. Xu Jun, Xiang Lei, Liu Qingshan, et al. Stacked Sparse Autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging, 2016, 35(1): 119-130.
26. Arevalo J, González F A, Ramos-Pollán R, et al. Convolutional neural networks for mammography mass lesion classi-fication//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milano: IEEE, 2015: 797-800.
27. Arevalo J, González F A, Ramos-Pollán R, et al. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed, 2016, 127: 248-257.
28. Fotin S V, Yin Y, Haldankar H, et al. Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches//SPIE Medical Imaging. California: International Society for Optics and Photonics, 2016, 9785: 97850X-97856.
29. Wu Kaizhi, Chen Xi, Ding Mingyue. Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Optik (Stuttg), 2014, 125(15): 4057-4063.
30. Liu Xiao, Shi Jun, Zhang Qi. Tumor classification by deep polynomial network and multiple kernel learning on small ultrasound image dataset//Machine Learning in Medical Imaging. Munich: Springer, 2015, 9352(0302-9743): 313-320.
31. Shi Jun, Zhou Shichong, Liu Xiao, et al. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing, 2016, 194: 87-94.
32. Abdel-Zaher A M, Eldeib A M. Breast cancer classification using deep belief networks. Expert Syst Appl, 2016, 46: 139-144.
33. Masood A, Al-Jumaily A, Anam K. Self-supervised learning model for skin cancer diagnosis//Neural Engineering (NER) 2015: 7th International IEEE/EMBS Conference. Montpellier: IEEE, 2015: 1012-1015.
34. Codella N, Cai J, Abedini M, et al. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images//Machine Learning in Medical Imaging. Munich: Springer, 2015, 9352(0302-9743): 118-126.

Previous Article
Research advances of fluid bio-mechanics in bone
Next Article
Research progress on the technique and materials for three-dimensional bio-printing

Journal of Biomedical Engineering

Research progress of computer-aided diagnosis in cancer based on deep learning and medical imaging

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content