Browse > Article
http://dx.doi.org/10.17946/JRST.2020.43.3.195

Artificial Intelligence Based Medical Imaging: An Overview  

Hong, Jun-Yong (Department of Multidisciplinary Radiological Science, Dongseo University)
Park, Sang Hyun (Department of Multidisciplinary Radiological Science, Dongseo University)
Jung, Young-Jin (Department of Radiology, Dongseo University)
Publication Information
Journal of radiological science and technology / v.43, no.3, 2020 , pp. 195-208 More about this Journal
Abstract
Artificial intelligence(AI) is a field of computer science that is defined as allowing computers to imitate human intellectual behavior, even though AI's performance is to imitate humans. It is grafted across software-based fields with the advantages of high accuracy and speed of processing that surpasses humans. Indeed, the AI based technology has become a key technology in the medical field that will lead the development of medical image analysis. Therefore, this article introduces and discusses the concept of deep learning-based medical imaging analysis using the principle of algorithms for convolutional neural network(CNN) and back propagation. The research cases application of the AI based medical imaging analysis is used to classify the various disease(such as chest disease, coronary artery disease, and cerebrovascular disease), and the performance estimation comparing between AI based medical imaging classifier and human experts.
Keywords
Artificial Intelligence; Medical Imaging; Deep Learning; Artificial Neural Network;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Harman M. The role of artificial intelligence in software engineering. In: 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE). IEEE. 2012 Jun:1-6.
2 Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018 Oct;2(1):35.   DOI
3 Trinder JC, Wang Y, Sowmya A, Palhang M. Artificial intelligence in 3-D feature extraction. In Automatic Extraction of Man-Made Objects from Aerial and Space Images. 2nd ed. Basel: Birkhauser; 1997.
4 Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017 Jun;19:221-48.   DOI
5 RavR D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017 Jan;21(1):4-21.   DOI
6 LeCun Y, Boser B, Denker JS, Henderson D. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1(4):541-51.   DOI
7 Neyshabur B, Tomioka R, Srebro N. In search of the real inductive bias: On the role of implicit regularization in deep learning. arXiv preprint arXiv: 1412.6614; 2014.
8 Yu K, Xu W, Gong Y. Deep learning with kernel regularization for visual recognition. Adv Neural Inf Process Syst. 2009:1889-96.
9 Kukacka J, Golkov V, Cremers D. Regularization for deep learning: A taxonomy. arXiv preprint arXiv:1710.10686; 2017.
10 Yoshida Y, Miyato T. Spectral norm regularization for improving the generalizability of deep learning. arXiv preprint arXiv:1705.10941; 2017.
11 Russell S, Norvig P. Artificial intelligence a modern approach. 3rd ed. New Jersey: Prentice Hall; 2009.
12 Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.
13 Vial A, Stirling D, Field M, Ros M, Ritz CH, Carolan, MG, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: A review. Translational Cancer Res. 2018;7(3):803-16.   DOI
14 Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps. 2018:323-50.
15 Jack Jr CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685-91.   DOI
16 Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, et al. MIRIAD-Public release of a multiple time point Alzheimer's MR imaging dataset. NeuroImage. 2013;70:33-6.   DOI
17 Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. Inbreast: Toward a full-field digital mammographic database. Acad Radiol. 2012;19(2):236-48.   DOI
18 Ding Q, Chen G, Zhang X, Huang Q, Ji H, Gao H. Low-dose CT with deep learning regularization via proximal forward backward splitting. Phys Med. 2020.
19 Shin YJ, Chang W, Ye JC, Kang E, Oh DY, Lee YJ. Low-dose abdominal CT using a deep learning-based denoising algorithm: A comparison with CT reconstructed with filtered back projection or iterative reconstruction algorithm. Korean J Radiol. 2020;21(3):356-64.   DOI
20 Xie D, Li Y, Yang H, Bai L, Wang T, Zhou F. Denoising arterial spin labeling perfusion MRI with deep machine learning. Magn Reson Imaging. 2020.
21 Weiss KR, Khoshgoftaar TM. Comparing transfer learning and traditional learning under domain class imbalance. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). 2017 Dec:337-43.
22 Ang JC, Mirzal A, Haron H, Hamed HNA. Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE/ACM Trans Comput Biol Bioinform. 2015;13(5):971-89.   DOI
23 Lin YZ, Nie ZH, Ma HW. Structural damage detection with automatic feature-extraction through deep learning. Computer-Aided Civ Inf Eng. 2017;32(12):1025-46.   DOI
24 Yang J, Zhao YQ, Chan JCW. Learning and transferring deep joint spectral-spatial features for hyperspectral classification. IEEE Trans Geosci Remote Sens. 2017;55(8):4729-42.   DOI
25 Yoo Y, Brosch T, Traboulsee A, Li DK, Tam R. Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In International Workshop on Machine Learning in Medical Imaging. 2014 Sep:117-24.
26 Cai Y, Landis M, Laidley DT, Kornecki A, Lum A, Li S. Multi-modal vertebrae recognition using transformed deep convolution network. Comput Med Imaging Graph. 2016 Jul;51:11-9.   DOI
27 Jaumard-Hakoun A, Xu K, Roussel-Ragot P, Dreyfus G, Denby, B. Tongue contour extraction from ultrasound images based on deep neural network. arXiv preprint arXiv:1605.05912; 2016.
28 Saito K. Deep learning starting from the bottom. Seoul: Hanbit Media; 2017.
29 Kim H, Nam H, Jung W, Lee J. Performance analysis of CNN frameworks for GPUs. In 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). 2017 Apr:55-64.
30 Agarap AF. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375; 2018.
31 Yi X, Babyn P. Sharpness-aware low-dose CT denoising using conditional generative adversarial network. J Digit Imaging. 2018;31(5):655-69.   DOI
32 Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A, Khalighi M. Ultra-low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs. Radiology. 2019; 290(3):649-56.   DOI
33 Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S. Generative adversarial nets. Adv Neural Inf Processing Systems. 2014:2672-80.
34 Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg CA, Isgum I. Deep MR to CT synthesis using unpaired data. In International Workshop on Simulation and Synthesis in Medical Imaging. 2017 Sep:14-23.
35 SRnchez I, Vilaplana V. Brain MRI super-resolution using 3D generative adversarial networks. arXiv preprint arXiv:1812.11440; 2018.
36 Ran M, Hu J, Chen Y, Chen H, Sun H, Zhou J. Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network. Medl Image Anal. 2019; 55:165-80.   DOI
37 Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q. Medical image synthesis with context-aware generative adversarial networks. In International Conference on Medical Image Computing and Computer-assisted Intervention. 2017 Sep:417-25.
38 Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine. 2018;15(11):e1002686.   DOI
39 Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB. Deep learning in medical imaging: General overview. Korean J Radiol. 2017 Aug;18(4):570-84.   DOI
40 Wakui Y, Wakui S. Math to understand deep learning. Seoul: Hanbit Media; 2018.
41 Shadmi R, Mazo V, Bregman-Amitai O, Elnekave E. Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT. In 2018 IEEE 15th International Symposium on Biomedical Imaging. 2018 Apr:24-8.
42 Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging. 2014;34(10):1993-2024.   DOI
43 Urban G, Bendszus M, Hamprecht F, Kleesiek J. Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI BraTS (brain tumor segmentation) challenge. Proceedings, Winning Contribution. 2014:31-5.
44 Zikic D, Ioannou Y, Brown M, Criminisi A. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS. 2014:36-9.
45 Oman O, Makela T, Salli E, Savolainen S, Kangasniemi M. 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. Eur Radiol Exp. 2019;3(1):8.   DOI
46 Yang W, Hong JY, Kim JY, Paik SH, Lee SH, Park JS, et al. A novel singular value decomposition-based denoising method in 4-dimensional computed tomography of the brain in stroke patients with statistical evaluation. Sensors. 2020;20:3063.   DOI
47 Choi H, Jin KH. Alzheimer's Disease Neuroimaging Initiative. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103-9.   DOI
48 Park HY, Pyeon D, Kim DH, Jung Y. Dynamic computed tomography based on spatio-temporal analysis in acute stroke: Preliminary study. J Radiol Sci Technol. 2016;39:543-7.   DOI
49 Kim D, Jung Y. Simulation study for feature identification of dynamic medical image reconstruction technique based on singular value decomposition. J Radiol Sci Technol. 2019;42:119-30.   DOI
50 Liu M, Zhang J, Adeli E, Shen D. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal. 2018;43:157-68.   DOI
51 Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging. 2016;3(3):034501.   DOI
52 McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.   DOI
53 Christe A, Peters AA, Drakopoulos D, Heverhagen JT, Geiser T, Stathopoulou T, et al. Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol. 2019;54(10):627.   DOI
54 Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal. 2018;47:45-67.   DOI
55 Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep. 2016;6(1):1-13.   DOI
56 Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chest X-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:2097-106.
57 Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther. 2015;8.
58 Dross PE, Sumner R, Tysowsky M, Aujero MP. International radiology for developing countries: The delivery of medical imaging services to a Honduran radiologic scarce zone. J Am Coll Radiol. 2014;11(12):1173-7.   DOI
59 Kwon DI. Only about 10 people in the emergency room nationwide who specialize in imaging medicine to read CT.MRI. Hankook Ilbo, 2017.11.20.