Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview

1 College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
2 Shanghai University of Medicine & Health Science, Shanghai 201308, China

Special Issues: Advanced Big Data Analysis for Precision Medicine

Computer-aided detection or diagnosis (CAD) has been a promising area of research over the last two decades. Medical image analysis aims to provide a more efficient diagnostic and treatment process for the radiologists and clinicians. However, with the development of science and technology, data interpretation manually in the conventional CAD systems has gradually become a challenging task. Deep learning methods, especially convolutional neural networks (CNNs), are successfully used as tools to solve this problem. This includes applications such as breast cancer diagnosis, lung nodule detection and prostate cancer localization. In this overview, the current state-of-the-art medical image analysis techniques in CAD research are presented, which focus on the convolutional neural network (CNN) based methods. The commonly used medical image databases in literature are also listed. It is anticipated that this paper can provide researchers in radiomics, precision medicine, and imaging grouping with a systematic picture of the CNN-based methods used in CAD research.
  Figure/Table
  Supplementary
  Article Metrics

Keywords computer-aided detection; computer-aided diagnosis; convolutional neural networks; deep learning; medical image analysis

Citation: Jun Gao, Qian Jiang, Bo Zhou, Daozheng Chen. Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview. Mathematical Biosciences and Engineering, 2019, 16(6): 6536-6561. doi: 10.3934/mbe.2019326

References

  • 1. G. J. Tearney, M. E. Brezinski and B. E. Bouma, In vivo endoscopic optical biopsy with optical coherence tomography, Science, 276 (1997), 2037–2039.
  • 2. R. William and Hendee, The impact of future technology on oncologic diagnosis: Oncologic imaging and diagnosis, Int. J. Radiat. Oncol. Biol. Phys., 9 (1983), 1851–1865.
  • 3. A. Heidenreich, F. Desgrandschamps and F. Terrier, Modern approach of diagnosis and management of acute flank pain: Review of all imaging modalities, Eur. Urol., 41 (2002), 351–362.
  • 4. R. E. Bunge and C. L. Herman, Usage of diagnostic imaging procedures: A nationwide hospital study, Radiology, 163 (1987), 569–573.
  • 5. G. B. A. Quekel, G. H. Kessels and R. Goei, Miss rate of lung cancer on the chest radiograph in clinical practice, Chest, 115 (1999), 720–724.
  • 6. F. Li, S. Shusuke and A. Hiroyuki, Lung cancers missed at low-dose helical CT screening in a general population: Comparison of clinical, histopathologic, and imaging findings, Radiology, 225 (2002), 673–683.
  • 7. Q. Li, F. Li and S. Kenji, Computer-aided diagnosis in thoracic CT, Semin. Ultrasound. Ct Mr, 26 (2005), 357–363.
  • 8. K. Suzuki, I. Sheu, M. Epstein, et al., An MTANN CAD for detection of polyps in false-negative CT colonography cases in a large multicenter clinical trial: Preliminary results, in Medical Imaging 2008: Computer-Aided Diagnosis, Med. Imaging. Int. Soc. Opt. Photonics., (2008).
  • 9. L. Ralph, Attempts to use computers as diagnostic aids in medical decision making: A thirty-year experience, Perspect. Biol. Med., 35(1992), 207–219.
  • 10. K. Doi, Current status and future potential of computer-aided diagnosis in medical imaging, Br. J. Radiol., 78(2005), S3–S19.
  • 11. K. Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential, Comput. Med. Imaging Graph., 31 (2007), 198–211.
  • 12. L. G. Maryellen, P. C. Heang and B. John, Anniversary paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM, Med. Phys., 35 (2008), 5799–5820.
  • 13. K. Doi, H. Macmahon, S. Katsuragawa, et al., Computer-aided diagnosis in radiology: Potential and pitfalls, Eur. J. Radiol., 31 (1999), 97–109.
  • 14. K. Kerlikowske, P. A. Carney, B. Geller, et al., Performance of screening mammography among women with and without a first-degree relative with breast cancer, Ann. Intern. Med., 133 (2000), 855–863.
  • 15. H. Sittek, K. Herrmann, C. Perlet, et al., Computer-aided diagnosis in mammography, Der Radiologe, 37 (1997), 610–616.
  • 16. R. Takahashi and Y. Kajikawa, Computer-aided diagnosis: A survey with bibliometric analysis, Int. J. Med. Inf., 101 (2017), 58–67.
  • 17. A. Mansoor, U. Bagci, B. Foster, et al., Segmentation and image analysis of abnormal lungs at CT: Current approaches, challenges, and future trends, Radiographics, 35 (2015), 1056–1076.
  • 18. S. Kenji, Computer-aided detection of lung cancer, in image-based computer-assisted radiation therapy, Springer, (2017), 9–40.
  • 19. A. El-Baz, G. M. Beache, G. Gimel'Farb, et al., Computer-aided diagnosis systems for lung cancer: Challenges and methodologies, Int. J. Biomed. Imaging, 2013 (2013), 1–46.
  • 20. M. Nishio and C. Nagashima, Computer-aided diagnosis for lung cancer: Usefulness of nodule heterogeneity, Acad. Radiol., 24 (2017), 328–336.
  • 21. M. Kawagishi, B. Chen, D. Furukawa, et al., A study of computer-aided diagnosis for pulmonary nodule: Comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists, Int. J. Comput. Assist. Radiol. Surg., 12 (2017), 1–10.
  • 22. A. O. D. C. Filho, A. C. Silva, A. C. D. Paiva, et al., Computer-aided diagnosis of lung nodules in computed tomography by using phylogenetic diversity, genetic algorithm, and SVM, J. Digit. Imaging, 30 (2017), 812–822.
  • 23. Y. Nomura, T. Higaki, M. Fujita, et al., Effects of iterative reconstruction algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose CT for lung cancer screening, Acad. Radiol., 24 (2017), 124–130.
  • 24. M. Liang, W. Tang, D. M. Xu, et al., Low-dose CT screening for lung cancer: Computer-aided detection of missed lung cancers, Radiology, 281 (2016), 279–288.
  • 25. D. Shen, G. Wu and H. I. Suk, Deep learning in medical image analysis, Annu. Rev. Biomed. Eng., 19 (2017), 221–248.
  • 26. Q. Song, L. Zhao, X. Luo, et al., Using deep learning for classification of lung nodules on computed tomography images, J. Healthcare Eng., 2017 (2017), 1–7.
  • 27. Y. Lecun, L. Bottou, Y. Bengio, et al., Gradient-based learning applied to document recognition, Proc. IEEE, 86 (1998), 2278–2324.
  • 28. K. Alex, S. Ilya and E. H. Geoffrey, ImageNet classification with deep convolution neural networks, 25th International Conference on Neural Information Processing Sys tems, Curran Associates Inc., (2012), 1097–1105. Available from: https://dl.acm.org/ci tation.cfm?id=2999257.
  • 29. T. Xiao, J. X. Zhang, K. Y. Yang, et al., Error-driven Incremental learning in deep convolutional neural network for large-scale image classification, ACM Multimedia, (2014), 177–186.
  • 30. C. Szegedy, W. Liu, Y. Q. Jia, et al., Going Deeper with Convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, (2015), 1–9. Available from: https://ieeexplore.ieee.org/document/7298594.
  • 31. S. T. Wu, S. H. Zhong and Y. Liu, Deep residual learning for image steganalysis, Multimedia. Tools. Appl., 77 (2017), 10437–10453.
  • 32. K. M. He, X. Y. Zhang, S. Q. Ren, et al., Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, in European Conference on Computer Vision (ECCV) 2014, Springer, (2014), 346–361.
  • 33. H. Greenspan, B. V. Ginneken and R. M. Summers, Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique, IEEE Trans. Med. Imaging, 35 (2016), 1153–1159.
  • 34. D. Wang, A. Khosla, R. Gargeya, et al., Deep Learning for Identifying Metastatic Breast Cancer, ArXiv 2016, (2016).
  • 35. D. Kumar, A. Wong and D. A. Clausi, Lung Nodule Classification Using Deep Features in CT Images, 12th Conference on Computer and Robot Vision, IEEE, (2015), 133–138. Available from: https://ieeexplore.ieee.org/document/7158331.
  • 36. F. Liu, C. Y. Wee, H. Chen, et al., Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification, Neuroimage, 84 (2014), 466–475.
  • 37. H. I. Suk, S. W. Lee and D. Shen, Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis, Neuroimage, 101 (2014), 569–582.
  • 38. S. Liu, S. D. Liu, W. D. Cai, et al., Early diagnosis of Alzheimer's disease with deep learning, 11th International Symposium on Biomedical Imaging (ISBI), IEEE, (2014), 1015–1018. Available from: https://ieeexplore.ieee.org/abstract/document/6868045.
  • 39. H. Fujita and D. Cimr, Computer Aided detection for fibrillations and flutters using deep convolutional neural network, Inf. Sci., 486 (2019), 231–239.
  • 40. L. C. S. Afonso, G. H. Rosa, C. R. Pereira, et al., A recurrence plot-based approach for Parkinson's disease identification, Future Gener. Comput. Syst., 94 (2019), 282–292.
  • 41. W. Li, Y. Zhao, X. Chen, et al., Detecting Alzheimer's Disease on Small Dataset: A Knowledge Transfer Perspective, IEEE J. Biomed. Health. Inf., 23 (2019), 1234–1242.
  • 42. L. Martin, J. Jendeberg, P. Thunberg, et al., Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks, Comput. Biol. Med., 97 (2018), 153–160.
  • 43. I. Sajid, M. U. Ghani, T. Saba, et al., Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN), Microsc. Res. Tech., 81 (2018), 419–427.
  • 44. F. Z. Liao, L. Ming, L. Zhe, et al., Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network, IEEE Trans. Neural Netw. Learn. Syst., 14 (2017), 1–12.
  • 45. A. Rajkomar, S. Lingam, A. G. Taylor, et al., High-throughput classification of radiographs using deep convolutional neural networks, J. Digit. Imaging, 30 (2017), 95–101.
  • 46. Z. N. Yan, Y. Q. Zhan, Z. G. Peng, et al., Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition, IEEE Trans. Med. Imaging, 35 (2016), 1332–1343.
  • 47. H. Pratt, F. Coenen, D. M. Broadbent, et al., Convolutional Neural Networks for Diabetic Retinopathy, Procedia. Comput. Sci., 90 (2016), 200–205.
  • 48. M. Gao, U. Bagci, L. Lu, et al., Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks, Comput. Methods Biomech. Biomed. Eng. Imaging Vis., 6 (2016), 1–6.
  • 49. H. C. Shin, H. R. Roth, M. Gao, et al., Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning, IEEE Trans. Med. Imaging, 35 (2016), 1285–1298.
  • 50. O. Ronneberger, P. Fischer and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, in MICCAI 2015, Springer, (2015), 234–241.
  • 51. O. F. Ahmad, A. Soares, E. B. Mazomenos, et al., Artificial intelligence and computer-aided diagnosis in colonoscopy: Current evidence and future directions, Lancet. Gastroenterol. Hepatol., 41 (2019), 71–80.
  • 52. I. González-Díaz, DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis, IEEE J. Biomed. Health. Inf., 23 (2019), 547–559.
  • 53. P. R. Jeyaraj, E. R. J. J. o. C. R. Samuel Nadar and C. Oncology, Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm, J. Cancer Res. Clin. Oncol., 145 (2019), 829–837.
  • 54. U. Raghavendra, H. Fujita, S. V. Bhandary, et al., Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images, Inf. Sci., 441 (2018), 41–49.
  • 55. U. Raghavendra, H. Fujita, A. Gudigar, et al., Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images, Biomed. Signal Process Control, 40 (2018), 324–334.
  • 56. E. Hosseini-Asl, M. Ghazal, A. Mahmoud, et al., Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network, Front Biosci (Landmark Ed), 23 (2018), 584–596.
  • 57. A. Farooq, S. M. Anwar, M. Awais, et al., A deep CNN based multi-class classification of Alzheimer's disease using MRI, 2017 IEEE International Conference on Imaging System and Techniques (IST), IEEE, (2018), 182–187. Available from: https://ieeexplore.ieee.org/document/8261460.
  • 58. G. V. Tulder and M. D. Bruijne, Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines, IEEE Trans. Med. Imaging, 35 (2016), 1262–1272.
  • 59. M. Anthimopoulos, S. Christodoulidis, L. Ebner, et al., Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network, IEEE Trans. Med. Imaging, 35 (2016), 1207–1216.
  • 60. A. Jalalian, S. Mashohor, R. Mahmud, et al., Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection, EXCLI J, 16 (2017), 113–137.
  • 61. M. L. Giger, N. Karssemeijer and J. A. Schnabel, Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer, Annu. Rev. Biomed. Eng., 15 (2013), 327–357.
  • 62. Q. H. Huang, F. B. Yang, L. Z. Liu, et al., Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis, Inf. Sci., 314 (2015), 293–310.
  • 63. T. C. Chiang, Y. S. Huang, R. T. Chen, et al., Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation, IEEE Trans. Med. Imaging, 38 (2018), 240–249.
  • 64. R. K. Samala, H. Chan, L. Hadjiiski, et al., Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets, IEEE Trans. Med. Imaging, 38 (2019), 686–696.
  • 65. A. S. Becker, M. Marcon, S. Ghafoor, et al., Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer, Invest. Radiol., 52 (2017), 434–440.
  • 66. X. R. Zhou, T. Kano, H. Koyasu, et al., Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN, in Medical Imaging 2017: Computer-Aided Diagnosis, Proc. SPIE, (2017).
  • 67. T. Kooi, G. B. Van, N. Karssemeijer, et al., Discriminating Solitary Cysts from Soft Tissue Lesions in Mammography using a Pretrained Deep Convolutional Neural Network, Med. Phys., 44 (2017), 1017–1027.
  • 68. A. B. Ayelet, L. Karlinsky, S. Alpert, et al., A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography, in Deep Learning and Data Labeling for Medical Applications, Springer, (2016), 197–205.
  • 69. J. G. Posada, D. M. Zapata and O. L. Q. Montoya, Detection and Diagnosis of Breast Tumors using Deep Convolutional Neural Networks, Conference Proceedings of the XVI I Latin American Conference on Automatic Control (2016), 11-17. Available from: http s://pdfs.semanticscholar.org/9566/d1f27a0e5f926827d3eaf8546dab51e40e21.pdf.
  • 70. R. K. Samala, H. P. Chan, L. Hadjiiski, et al., Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography, Med. Phys., 43 (2016), 6654.
  • 71. N. Dhungel, G. Carneiro and A. P. Bradley, The Automated Learning of Deep Features for Breast Mass Classification from Mammograms, in MICCAI 2016,Springer, (2016), 106–114.
  • 72. Y. J. Zhou, J. X. Xu, Q. G. Liu, et al., A Radiomics Approach with CNN for Shear-wave Elastography Breast Tumor Classification, IEEE Trans. Biomed. Eng., 65 (2018), 1935–1942.
  • 73. F. Gao, T. Wu, J. Li, et al., SD-CNN: A Shallow-Deep CNN for Improved Breast Cancer Diagnosis, Comput Med Imaging Graph, 70 (2018), 53–62.
  • 74. J. Li, M. Fan, J. Zhang, et al., Discriminating between benign and malignant breast tumors using 3D convolutional neural network in dynamic contrast enhanced MR images, in Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, Proc. SPIE, (2017).
  • 75. T. Kooi, G. Litjens, B. V. Ginneken, et al., Large scale deep learning for computer aided detection of mammographic lesions, Med. Image. Anal., 35 (2017), 303–312.
  • 76. R. Samala, H. P. Chan, L. Hadjiiski, et al., Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis, in Medical Imaging 2016: Computer-Aided Diagnosis, Proc. SPIE, (2016).
  • 77. D. M. Parkin, Global cancer statistics in the year 2000, Lancet Oncol., 2 (2001), 533–543.
  • 78. A. Motohiro, H. Ueda, H. Komatsu, et al., Prognosis of non-surgically treated, clinical stage I lung cancer patients in Japan, Lung Cancer, 36 (2002), 65–69.
  • 79. K. L. Hua, C. H. Hsu, S. C. Hidayati, et al., Computer-aided classification of lung nodules on computed tomography images via deep learning technique, Onco. Targets Ther., 8 (2015), 2015–2022.
  • 80. Z. H. Shi, H. Hao, M. H. Zhao, et al., A deep CNN based transfer learning method for false positive reduction, Multimed. Tools Appl., 78 (2018), 1017–1033.
  • 81. F. Ciompi, B. D. Hoop, S. J. V. Riel, et al., Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box, Med. ImageAnal., 26 (2015), 195–202.
  • 82. M. Nishio, O. Sugiyama, M. Yakami, et al., Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning, PLoS One, 13 (2018), e0200721.
  • 83. G. Savitha and P. Jidesh, Lung Nodule Identification and Classification from Distorted CT Images for Diagnosis and Detection of Lung Cancer, in Machine Intelligence and Signal Analysis, Springer, (2019), 11–23.
  • 84. X. Z. Zhao, L. Y. Liu, S. Qi, et al., Agile convolutional neural network for pulmonary nodule classification using CT images, Int. J. Comput. Assist Radiol. Surg., 13 (2018), 585–595.
  • 85. J. Ding, A. Li, Z. Q. Hu, et al., Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks, in MICCAI 2017,Springer, (2017), 559–567.
  • 86. Q. Dou, H. Chen, Y. M. Jin, et al., Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning, in MICCAI 2017,Springer, (2017), 630–638.
  • 87. J. Z. Cheng, D. Ni, Y. H. Chou, et al., Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans, Sci. Rep., 6 (2016), 24454.
  • 88. K. Liu and G. Kang, 3D multi-view convolutional neural networks for lung nodule classification, PLoS One, 12 (2017), e0188290.
  • 89. R. Dey, Z. J. Lu and H. Yi, Diagnostic Classification Of Lung Nodules Using 3D Neural Networks, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI), IEEE, (2018), 774–778. Available from: https://ieeexplore.ieee.org/document/8363687.
  • 90. D. Anton, K. Ramil, K. Adil, et al., Large Residual Multiple View 3D CNN for False Positive Reduction in Pulmonary Nodule Detection, 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE, (2017). Available from: https://ieeexplore.ieee.org/document/8058549.
  • 91. W. Li, P. Cao, D. Z. Zhao, et al., Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images, Comput. Math. Method. Med., 2016 (2016), 1–7.
  • 92. R. L. Siegel, K. D. Miller and A. Jemal, Cancer Statistics, 2017, CA Cancer J. Clin., 67 (2017), 7–30.
  • 93. R. Chou, J. M. Croswell, D. Tracy, et al., Screening for prostate cancer: A review of the evidence for the U.S. Preventive Services Task Force, Ann. Intern. Med., 137 (2011), 55–73.
  • 94. A. Rampun, L. Zheng, P. Malcolm, et al., Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone, Phys. Med. Biol., 61 (2016), 4796–4825.
  • 95. W. Li, J. Li, K. V. Sarma, et al., Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images, IEEE Trans. Med. Imaging, 38 (2018), 945–954.
  • 96. E. Leng, J. C. Henriksen, A. E. Rizzardi, et al., Signature maps for automatic identification of prostate cancer from colorimetric analysis of H&E and IHC-stained histopathological specimens, Sci. Rep., 9 (2019), 6992.
  • 97. Q. Chen, X. Xu, S. L. Hu, et al., A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans, in Medical Imaging 2017: Computer-Aided Diagnosis, Proc. SPIE, (2017).
  • 98. Y. Song, Y. D. Zhang, X. Yan, et al., Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI, J. Magn. Reson. Imaging, 48 (2018), 1570–1577.
  • 99. Z. Wang, C. Liu, D. Cheng, et al., Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network, IEEE Trans. Med. Imaging, 37 (2018), 1127–1139.
  • 100. J. Ishioka, Y. Matsuoka, S. Uehara, et al., Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm, BJU Int., 122 (2018), 411–417.
  • 101. S. Kohl, D. Bonekamp, H.P. Schlemmer, et al., Adversarial Networks for the Detection of Aggressive Prostate Cancer, in ArXiv, (2017). Available from: https://arxiv.org/abs/17 02.08014.
  • 102. X. Yang, C. Y. Liu, Z. W. Wang, et al., Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI, Med. Image Anal., 42 (2017), 212–227.
  • 103. T. K. Jin and S. M. Hewitt, Nuclear Architecture Analysis of Prostate Cancer via Convolutional Neural Networks, IEEE Access, 5 (2017), 18526–18533.
  • 104. X. Wang, W. Yang, J. Weinreb, et al., Searching for prostate cancer by fully automated magnetic resonance imaging classification: Deep learning versus non-deep learning, Sci. Rep., 7 (2017), 15415.
  • 105. S. F. Liu, H. X. Zheng, Y. Feng, et al., Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI, in Medical Imaging 2017: Computer-Aided Diagnosis, Proc. SPIE, (2017).
  • 106. M. H. Le, J. Y. Chen, L. Wang, et al., Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks, Phys. Med. Biol., 62 (2017), 6497–6514.
  • 107. X. Yang, Z. W. Wang, C. Y. Liu, et al., Joint Detection and Diagnosis of Prostate Cancer in Multi-parametric MRI Based on Multimodal Convolutional Neural Networks, in MICCAI 2017,Springer, (2017), 426–434.
  • 108. M. F. McNitt-Gray, S. G. Armato, C. R. Meyer, et al., The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation, Acad. Radiol., 14 (2007), 1464–1474.
  • 109. A. P. Reeves, A. M. Biancardi, D. Yankelevitz, et al., A public image database to support research in computer aided diagnosis, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2009(2009), 3715–3718.
  • 110. K. S. Man, C. Ramachandran, A. Yianni, et al., Automatic pectoral muscle segmentation on mediolateral oblique view mammograms, IEEE Trans. Med. Imaging, 23 (2004), 1129–1140.
  • 111. D. Saraswathi and E. Srinivasan, An ensemble approach to diagnose breast cancer using fully complex-valued relaxation neural network classifier, Int. J. Biomed. Eng. Technol., 15 (2014), 243–260.
  • 112. M. Lamard, G. Cazuguel, G. Quellec, et al., Content Based Image Retrieval based on Wavelet Transform coefficients distribution, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, (2007). Available from: https://ieeexplore.ieee.org/document/4353347.
  • 113. G. D. Tourassi, in Intelligent Paradigms for Healthcare Enterprises, Current Status of Computerized Decision Support Systems in Mammography, Springer, (2005), 173–208.
  • 114. R. M. Rangayyan, F. J. Ayres and J. E. L. Desautels, A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs, J. Franklin Inst., 344 (2007), 312–348.
  • 115. M. Sundaram, K. Ramar, N. Arumugam, et al., Histogram Modified Local Contrast Enhancement for mammogram images, Appl. Soft. Comput., 11 (2011), 5809–5816.
  • 116. P. M. Bolton, S. L. James, J. M. Davidson, et al., Proceedings: Diagnostic and prognostic significance of immune competence testing in patients with breast cancer, Br. J. Surg., 61 (1974), 325–326.
  • 117. N. Pérez, M. A. Guevara and A. Silva, Improving Breast Cancer Classification with Mammography, supported on an appropriate Variable Selection Analysis, in Medical Imaging 2013: Computer-Aided Diagnosis, Proc. SPIE, (2013).
  • 118. T. Messay, R. C. Hardie and T. R. Tuinstra, Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset, Med. Image Anal., 22 (2015), 48–62.
  • 119. W. S. Wang, J. W. Luo, X. D. Yang, et al., Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative, Acad. Radiol., 22 (2015), 488–495.
  • 120. Y. Rowena, C. I. Henschke, D. F. Yankelevitz, et al., CT screening for lung cancer: Alternative definitions of positive test result based on the national lung screening trial and international early lung cancer action program databases, Radiology, 273 (2014), 591–596.
  • 121. M. Oudkerk and M. A. Heuvelmans, Screening for lung cancer by imaging: The Nelson study, JBR-BTR, 96 (2013), 163–166.
  • 122. Z. Y. Ru, X. Xie, H. J. d. Koning, et al., NELSON lung cancer screening study, Cancer Imaging, 11 (2011), S79–S84.
  • 123. G. B. Van, A. V. D. Van, T. Duindam, et al., Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study, Med. Image Anal., 14 (2010), 707–722.
  • 124. L. Geert, D. Oscar, B. Jelle, et al., Computer-aided detection of prostate cancer in MRI, IEEE Trans. Med. Imaging, 33 (2014), 1083–1092.
  • 125. K. W. Clark, B. A. Vendt, K. E. Smith, et al., The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, J. Digit. Imaging, 26 (2013), 1045–1057.
  • 126. V. M. Gonçalves, M. E. Delamaro and F. L. S. Nunes, A systematic review on the evaluation and characteristics of computer-aided diagnosis systems, Rev. Bras. Eng. Bioméd., 30 (2014), 355–383.
  • 127. J. Ma, F. Wu, J. Zhu, et al., A pre-trained convolutional neural network based method for thyroid nodule diagnosis, Ultrasonics, 73 (2017), 221–230.
  • 128. W. Sun, T. B. Tseng, J. Zhang, et al., Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data, Comput. Med. Imaging Graph., 57 (2017), 4–9.
  • 129. W. L. Chen, Y. Zhang, J. J. He, et al., Prostate Segmentation using 2D Bridged U-net, ArXiv 2018, (2018).
  • 130. F. Milletari, N. Navab and S. A. Ahmadi, V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, 2016 Fourth International Conference on 3D Vision (3DV), IEEE, (2016). Available from: https://ieeexplore.ieee.org/document/7785132.
  • 131. S. Hussain, S. M. Anwar and M. Majid, Segmentation of glioma tumors in brain using deep convolutional neural network, Neurocomputing, 282 (2017), 248–261.
  • 132. R. Caruana, in Machine Learning, Multitask Learning, Springer, (1997), 41–75.
  • 133. I. Guyon, G. Dror, V. Lemaire, et al., Unsupervised and transfer learning challenge, The 2011 International Joint Conference on Neural Networks, IEEE, (2011).Available from: https://ieeexplore.ieee.org/document/6033302.
  • 134. R. J. Gillies, P. E. Kinahan and H. Hricak, Radiomics: Images Are More than Pictures, They Are Data, Radiology, 278 (2016), 563–577.
  • 135. T. Tran and R. Kavuluru, Predicting Mental Conditions Based on "History of Present Illness" in Psychiatric Notes with Deep Neural Networks, J. Biomed. Inf., 75 (2017), S138–S148.

 

This article has been cited by

  • 1. Nikolaos Papandrianos, Elpiniki Papageorgiou, Athanasios Anagnostis, Anna Feleki, A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans, Applied Sciences, 2020, 10, 3, 997, 10.3390/app10030997

Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved