Advertisement

The present and future of deep learning in radiology

      Highlights

      • This review focuses different aspects of deep learning applications in radiology.
      • This paper covers evolution of deep learning, its potentials, risk and safety issues.
      • This review covers some deep learning techniques already applied.
      • It gives an overall view of impact of deep learning in the medical imaging industry.

      Abstract

      The advent of Deep Learning (DL) is poised to dramatically change the delivery of healthcare in the near future. Not only has DL profoundly affected the healthcare industry it has also influenced global businesses. Within a span of very few years, advances such as self-driving cars, robots performing jobs that are hazardous to human, and chat bots talking with human operators have proved that DL has already made large impact on our lives. The open source nature of DL and decreasing prices of computer hardware will further propel such changes. In healthcare, the potential is immense due to the need to automate the processes and evolve error free paradigms. The sheer quantum of DL publications in healthcare has surpassed other domains growing at a very fast pace, particular in radiology. It is therefore imperative for the radiologists to learn about DL and how it differs from other approaches of Artificial Intelligence (AI). The next generation of radiology will see a significant role of DL and will likely serve as the base for augmented radiology (AR). Better clinical judgement by AR will help in improving the quality of life and help in life saving decisions, while lowering healthcare costs.
      A comprehensive review of DL as well as its implications upon the healthcare is presented in this review. We had analysed 150 articles of DL in healthcare domain from PubMed, Google Scholar, and IEEE EXPLORE focused in medical imagery only. We have further examined the ethic, moral and legal issues surrounding the use of DL in medical imaging.

      Abbreviations:

      ANN (artificial neural network), cIMT (carotid intima-media thickness), CNN (convolution neural network), CT (computed tomography), DBN (deep belief network), DL (deep learning), ELM (extreme learning machine), FCN (fully connected network), FLD (fatty liver disease), GPU (graphics processing unit), LI (lumen-intima), MA (media adventitia), ML (machine learning), MLP (multi-layer perceptron), MRI (magnetic resonance imaging), RNN (residual neural network), TC (tissue characterization)

      Keywords

      To read this article in full you will need to make a payment

      References

        • Hubel David H.
        • Wiesel T.N.
        Shape and arrangement of columns in cat’s striate cortex.
        J. Physiol. 1963; 165: 559-568
        • Nasrabadi Nasser M.
        Pattern recognition and machine learning.
        J. Electron. Imaging. 2007; 16049901
        • Goodfellow Ian
        • Bengio Yoshua
        • Courville Aaron
        • Bengio Yoshua
        Deep Learning. Vol. 1. Cambridge MIT press, 2016
        • Rosenblatt Frank
        The perceptron: a probabilistic model for information storage and organization in the brain.
        Psychol. Rev. 1958; 65: 386
        • Haykin Simon
        Neural Networks, a Comprehensive Foundation. No. BOOK.
        Macmilan, 1994
        • Cybenko George
        Approximation by superpositions of a sigmoidal function, Mathematics of control.
        Conf. Rec. Asilomar Conf. Signals Syst. Comput. 1989; 2: 303-314
        • Park Jooyoung
        • Sandberg Irwin W.
        Universal approximation using radial-basis-function networks.
        Neural Comput. 1991; 3: 246-257
        • Vapnik V.N.
        An overview of statistical learning theory.
        IEEE Trans. Neural Netw. 1999; 10: 988-999
        • Huang Guang-Bin
        • Zhu Qin-Yu
        • Siew Chee-Kheong
        Extreme learning machine: theory and applications.
        Neurocomputing. 2006; 70: 489-501
        • Kumar Satish
        Neural Networks: A Classroom Approach.
        Tata McGraw-Hill Education, 2004
        • Hinton Geoffrey E.
        • Salakhutdinov Ruslan R.
        Reducing the dimensionality of data with neural networks.
        Science. 2006; 313: 504-507
        • Acharya U.R.
        • Sree S.V.
        • Ribeiro R.
        • Krishnamurthi G.
        • Marinho R.T.
        • Sanches J.
        • Suri J.S.
        Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm.
        Med. Phys. 2012; 39: 4255-4264
        • Saba L.
        • Dey N.
        • Ashour A.S.
        • Samanta S.
        • Nath S.S.
        • Chakraborty S.
        • Sanches J.
        • Kumar D.
        • Marinho R.
        • Suri J.S.
        Automated stratification of liver disease in ultrasound: an online accurate feature classification paradigm.
        Comput. Methods Programs Biomed. 2016; 130: 118-134
        • Kuppili V.
        • Biswas M.
        • Sreekumar A.
        • Suri H.S.
        • Saba L.
        • Edla D.R.
        • Marinhoe R.T.
        • Sanches J.M.
        • Suri J.S.
        Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization.
        J. Med. Syst. 2017; 41: 152
        • Acharya U.R.
        • Sree S.V.
        • Kulshreshtha S.
        • Molinari F.
        • Koh J.E.W.
        • Saba L.
        • Suri J.S.
        GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization.
        Technol. Cancer Res. Treat. 2014; 13: 529-539
        • Acharya U.R.
        • Krishnan M.M.R.
        • Saba L.
        • Molinari F.
        • Guerriero S.
        • Suri J.S.
        Ovarian tumor characterization using 3D ultrasound.
        Ovarian Neoplasm Imaging. Springer, Boston, MA2013: 399-412
        • Acharya U.R.
        • Sree S.V.
        • Saba L.
        • Molinari F.
        • Guerriero S.
        • Suri J.S.
        Ovarian tumor characterization and classification using ultrasound—a new online paradigm.
        J. Digit. Imaging. 2013; 26: 544-553
        • Acharya R.U.
        • Faust O.
        • Alvin A.P.C.
        • Sree S.V.
        • Molinari F.
        • Saba L.
        • Nicolaides A.
        • Suri J.S.
        Symptomatic vs. asymptomatic plaque classification in carotid ultrasound.
        J. Med. Syst. 2012; 36: 1861-1871
        • Saba L.
        • Jain P.K.
        • Suri H.S.
        • Ikeda N.
        • Araki T.
        • Singh B.K.
        • Nicolaides A.
        • Shafique S.
        • Gupta A.
        • Laird J.R.
        • Suri J.S.
        Plaque tissue morphology-based stroke risk stratification using carotid ultrasound: a polling-based PCA learning paradigm.
        J. Med. Syst. 2007; 41: 98
        • Delsanto S.
        • Molinari F.
        • Giustetto P.
        • Liboni W.
        • Badalamenti S.
        • Suri J.S.
        Characterization of a completely user-independent algorithm for carotid artery segmentation in 2-D ultrasound images.
        IEEE Trans. Instrum. Meas. 2007; 56: 1265-1274
        • Suri Jasjit
        • Saba Luca
        • Dey Nilanjan
        • Samanta Sourav
        • Nath Siddhartha Sankar
        • Chakraborty Sayan
        • Kumar Dinesh
        • Sanches João
        2083667 Online system for liver disease classification in ultrasound.
        Ultrasound Med. Biol. 2015; 41: S18
        • Molinari F.
        • Zeng G.
        • Suri J.S.
        Greedy technique and its validation for fusion of two segmentation paradigms leads to an accurate intima–media thickness measure in plaque carotid arterial ultrasound.
        J. Vasc. Ultrasound. 2010; 34: 63-73
        • Saba L.
        • Lippo R.S.
        • Tallapally N.
        • Molinari F.
        • Montisci R.
        • Mallarini G.
        • Suri J.S.
        Evaluation of carotid wall thickness by using computed tomography and semiautomated ultrasonographic software.
        J. Vasc. Ultrasound. 2011; 35: 136-142
        • Suri J.S.
        Two-dimensional fast magnetic resonance brain segmentation.
        IEEE Eng. Med. Biol. Mag. 2011; 20: 84-95
        • Suri J.S.
        Computer vision, pattern recognition and image processing in left ventricle segmentation: the last 50 years.
        Pattern Anal. Appl. 2000; 3: 209-242
        • Paninski Liam
        • Pillow Jonathan
        • Lewi Jeremy
        Statistical models for neural encoding, decoding, and optimal stimulus design.
        Prog. Brain Res. 2007; 165: 493-507
        • Nishimoto S.
        • Vu A.T.
        • Naselaris T.
        • Benjamini Y.
        • Yu B.
        • Gallant J.L.
        Reconstructing visual experiences from brain activity evoked by natural movies.
        Curr. Biol. 2011; 21: 1641-1646
        • Kay K.N.
        • Naselaris T.
        • Prenger R.J.
        • Gallant J.L.
        Identifying natural images from human brain activity.
        Nature. 2008; 452: 352
        • Jain Anil K.
        • Farrokhnia Farshid
        Unsupervised texture segmentation using Gabor filters.
        Pattern Recognit. 1991; 24: 1167-1186
        • Krizhevsky A.
        • Sutskever I.
        • Hinton G.E.
        Imagenet classification with deep convolutional neural networks.
        Adv. Neural Inf. Process. Syst. 2012; 2012: 1097-1105
        • LeCun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436
        • Zeiler M.D.
        • Fergus R.
        Visualizing and understanding convolutional networks.
        European Conference on Computer Vision. 2014; : 818-833
        • Simonyan Karen
        • Zisserman Andrew
        Very Deep Convolutional Networks for Large-Scale Image Recognition.
        arXiv preprint arXiv:1409.1556, 2014
        • Szegedy C.
        • Liu W.
        • Jia Y.
        • Sermanet P.
        • Reed S.
        • Anguelov D.
        • Erhan D.
        • Vanhoucke V.
        • Rabinovich A.
        Going deeper with convolutions.
        Cvpr. 2015;
        • He K.
        • Zhang X.
        • Ren S.
        • Sun J.
        Deep residual learning for image recognition.
        Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 770-778
        • Hu J.
        • Shen L.
        • Sun G.
        Squeeze-and-excitation Networks.
        arXiv preprint arXiv:1709.01507, 2017
        • Ching T.
        • Himmelstein D.S.
        • Beaulieu-Jones B.K.
        • Kalinin A.A.
        • Do B.T.
        • Way G.P.
        • Ferrero E.
        • Agapow P.M.
        • Zietz M.
        • Hoffman M.M.
        • Xie W.
        Opportunities and obstacles for deep learning in biology and medicine.
        bioRxiv. 2018; 142760
        • Yamins Daniel L.K.
        • Hong Ha
        • Cadieu Charles F.
        • Solomon Ethan A.
        • Seibert Darren
        • DiCarlo James J.
        Performance-optimized hierarchical models predict neural responses in higher visual cortex.
        Proc. Natl. Acad. Sci. 2014; 111: 8619-8624
        • Cadieu Charles F.
        • Hong Ha
        • Yamins Daniel L.K.
        • Pinto Nicolas
        • Ardila Diego
        • Solomon Ethan A.
        • Majaj Najib J.
        • DiCarlo James J.
        Deep neural networks rival the representation of primate IT cortex for core visual object recognition.
        PLoS Comput. Biol. 2014; 10e1003963
        • Güçlü Umut
        • van Gerven Marcel AJ
        Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream.
        J. Neurosci. 2015; 35: 10005-10014
        • Hasson Uri
        • Nir Yuval
        • Levy Ifat
        • Fuhrmann Galit
        • Malach Rafael
        Intersubject synchronization of cortical activity during natural vision.
        Science. 2004; 303: 1634-1640
        • Lu Kun-Han
        • Hung Shao-Chin
        • Wen Haiguang
        • Marussich Lauren
        • Liu Zhongming
        Influences of high-level features, gaze, and scene transitions on the reliability of BOLD responses to natural movie stimuli.
        PLoS One. 2016; 11e0161797
        • Pan Sinno Jialin
        • Yang Qiang
        A survey on transfer learning.
        IEEE Trans. Knowl. Data Eng. 2010; 22: 1345-1359
        • Bengio Yoshua
        Deep learning of representations for unsupervised and transfer learning.
        Proceedings of ICML Workshop on Unsupervised and Transfer Learning. 2012: 17-36
        • Mesnil Grégoire
        • Dauphin Yann
        • Glorot Xavier
        • Rifai Salah
        • Bengio Yoshua
        • Goodfellow Ian
        • Lavoie Erick
        • et al.
        Unsupervised and transfer learning challenge: a deep learning approach.
        Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop. 2011; 27: 97-111
        • Sun Yi
        • Wang Xiaogang
        • Tang Xiaoou
        Deep learning face representation from predicting 10,000 classes.
        Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014: 1891-1898
        • Litjens Geert
        • Kooi Thijs
        • Bejnordi Babak Ehteshami
        • Setio Arnaud Arindra Adiyoso
        • Ciompi Francesco
        • Ghafoorian Mohsen
        • Van Der Laak Jeroen Awm
        • Van Ginneken Bram
        • Sánchez Clara I.
        A survey on deep learning in medical image analysis.
        Med. Image Anal. 2017; 42: 60-88
        • Sun Wenqing
        • Zheng Bin
        • Qian Wei
        Computer aided lung cancer diagnosis with deep learning algorithms.
        Medical Imaging 2016: Computer-Aided Diagnosis. 9785. International Society for Optics and Photonics, 2016: 97850Z
        • Hua Kai-Lung
        • Hsu Che-Hao
        • Hidayati Shintami Chusnul
        • Cheng Wen-Huang
        • Chen Yu-Jen
        Computer-aided classification of lung nodules on computed tomography images via deep learning technique.
        Oncol. Ther. 2015; 8
        • Milletari Fausto
        • Navab Nassir
        • Ahmadi Seyed-Ahmad
        V-net: fully convolutional neural networks for volumetric medical image segmentation.
        3D Vision (3DV), 2016 Fourth International Conference on. 2016; : 565-571
        • Prasoon Adhish
        • Petersen Kersten
        • Igel Christian
        • Lauze François
        • Dam Erik
        • Nielsen Mads
        Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.
        International Conference on Medical Image Computing and Computer-Assisted Intervention. 2013; : 246-253
        • Ngo Tuan Anh
        • Lu Zhi
        • Carneiro Gustavo
        Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.
        Med. Image Anal. 2017; 35: 159-171
        • Betechuoh Brain Leke
        • Marwala Tshilidzi
        • Tettey Thando
        Autoencoder networks for HIV classification.
        Curr. Sci. 2006; : 1467-1473
        • Wei Qi
        • Ren Yinhao
        • Hou Rui
        • Shi Bibo
        • Lo Joseph Y.
        • Carin Lawrence
        Anomaly detection for medical images based on a one-class classification.
        Medical Imaging 2018: Computer-Aided Diagnosis. vol. 10575. International Society for Optics and Photonics, 2018: 105751M
        • Kallenberg Michiel
        • Petersen Kersten
        • Nielsen Mads
        • Ng Andrew Y.
        • Diao Pengfei
        • Igel Christian
        • Vachon Celine M.
        • et al.
        Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring.
        IEEE Trans. Med. Imaging. 2016; 35: 1322-1331
        • Korfiatis Panagiotis
        • Kline Timothy L.
        • Erickson Bradley J.
        Automated segmentation of hyperintense regions in FLAIR MRI using deep learning.
        Tomography: a journal for imaging research. 2016; 2: 334
        • O’Neil Richard
        Convolution operators and $ L (p, q) $ spaces.
        Duke Math. J. 1963; 30: 129-142
        • Yu Fisher
        • Wang Dequan
        • Shelhamer Evan
        • Darrell Trevor
        Deep Layer Aggregation.
        arXiv preprint arXiv:1707.06484, 2017
        • Noh Hyeonwoo
        • Hong Seunghoon
        • Han Bohyung
        Learning deconvolution network for semantic segmentation.
        Proceedings of the IEEE International Conference on Computer Vision. 2015: 1520-1528
        • Liou Cheng-Yuan
        • Cheng Wei-Chen
        • Liou Jiun-Wei
        • Liou Daw-Ran
        Autoencoder for words.
        Neurocomputing. 2014; 139: 84-96
        • Vincent Pascal
        • Larochelle Hugo
        • Lajoie Isabelle
        • Bengio Yoshua
        • Manzagol Pierre-Antoine
        Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion.
        J. Mach. Learn. Res. 2010; 11: 3371-3408
        • Hinton Geoffrey E.
        Deep belief networks.
        Scholarpedia. 2009; 4: 5947
        • Biswas M.
        • Kuppili V.
        • Saba L.
        • Edla D.R.
        • Suri H.S.
        • Cuadrado-Godia E.
        • Laird J.R.
        • Marinhoe R.T.
        • Sanches J.M.
        • Nicolaides A.
        • Suri J.S.
        State-of-the-art review on deep learning in medical imaging.
        Front. Biosci. (Landmark Ed). 2019; 1 (5): 392-4264
      1. Sra Suvrit Nowozin Sebastian Wright Stephen J. Optimization for Machine Learning. Mit Press, 2012
        • Hu Peijun
        • Wu Fa
        • Peng Jialin
        • Liang Ping
        • Kong Dexing
        Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.
        Phys. Med. Biol. 2016; 61: 8676
        • Li Wen
        • Jia Fucang
        • Hu Qingmao
        Automatic segmentation of liver tumor in CT images with deep convolutional neural networks.
        J. Comput. Commun. 2015; 3: 146
        • Browning Jeffrey D.
        • Szczepaniak Lidia S.
        • Dobbins Robert
        • Horton Jay D.
        • Cohen Jonathan C.
        • Grundy Scott M.
        • Hobbs Helen H.
        Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity.
        Hepatology. 2004; 40: 1387-1395
        • Biswas Mainak
        • Kuppili Venkatanareshbabu
        • Edla Damodar Reddy
        • Suri Harman S.
        • Saba Luca
        • Marinhoe Rui Tato
        • Miguel Sanches J.
        • Suri Jasjit S.
        Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.
        Comput. Methods Programs Biomed. 2018; 155: 165-177
        • Szegedy C.
        • Vanhoucke V.
        • Ioffe S.
        • Shlens J.
        • Wojna Z.
        Rethinking the inception architecture for computer vision.
        Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2818-2826
        • Lloyd-Jones Donald
        • Adams Robert J.
        • Brown Todd M.
        • Carnethon Mercedes
        • Dai Shifan
        • Giovanni De Simone T.
        • Ferguson Bruce
        • et al.
        Heart disease and stroke statistics—2010 update.
        Circulation. 2010; 121: e46-e215
        • Biswas Mainak
        • Kuppili Venkatanareshbabu
        • Araki Tadashi
        • Edla Damodar Reddy
        • Godia Elisa Cuadrado
        • Saba Luca
        • Suri Harman S.
        • et al.
        Deep learning strategy for accurate carotid intima-media thickness measurement: an ultrasound study on Japanese diabetic cohort.
        Comput. Biol. Med. 2018;
        • Long Jonathan
        • Shelhamer Evan
        • Darrell Trevor
        Fully convolutional networks for semantic segmentation.
        Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3431-3440
        • Nielsen Anne
        • Hansen Mikkel Bo
        • Tietze Anna
        • Mouridsen Kim
        Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning.
        Stroke. 2018; 49: 1394-1401
        • Straka Matus
        • Albers Gregory W.
        • Bammer Roland
        Real‐time diffusion‐perfusion mismatch analysis in acute stroke.
        J. Magn. Reson. Imaging. 2010; 32: 1024-1037
        • Song Y.
        • Zhang Y.D.
        • Yan X.
        • Liu H.
        • Zhou M.
        • Hu B.
        • Yang G.
        Computer‐aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.
        J. Magn. Reson. Imaging. 2018;
        • González Germán
        • Ash Samuel Y.
        • Vegas-Sánchez-Ferrero Gonzalo
        • Onieva Jorge Onieva
        • Rahaghi Farbod N.
        • Ross James C.
        • Díaz Alejandro
        • Estépar Raúl San José
        • Washko George R.
        Disease staging and prognosis in smokers using deep learning in chest computed tomography.
        Am. J. Respir. Crit. Care Med. 2018; 197: 193-203
      2. Available Online. http://www.copdgene.org/.

        • Vestbo J.ørgen
        • Anderson Wayne
        • Coxson Harvey O.
        • Crim Courtney
        • Dawber Ffyona
        • Edwards Lisa
        • Hagan Gerry
        • et al.
        Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE).
        Eur. Respir. J. 2008; 31: 869-873
        • Bojarski Mariusz
        • Davide Del Testa Daniel Dworakowski
        • Firner Bernhard
        • Flepp Beat
        • Goyal Prasoon
        • Jackel Lawrence D.
        • et al.
        End to End Learning for Self-driving Cars.
        arXiv preprint arXiv:1604.07316, 2016
        • Silberg Gary
        • Wallace Richard
        • Matuszak G.
        • Plessers J.
        • Brower C.
        • Subramanian Deepak
        Self-driving cars: the next revolution, white paper.
        KPMG LLP Cent. Autom. Res. 2012; : 36
        • Araujo Luis
        • Manson K.
        • Spring Martin
        Self-Driving Cars, A Case Study in Making New Markets. 9. Big Innovation Centre, London, UK2012
        • Narla S.R.
        The evolution of connected vehicle technology: from smart drivers to smart cars to self-driving cars.
        ITE J. 2013; 83: 22
        • Newton Casey
        Uber will eventually replace all its drivers with self-driving cars.
        Verge. 2014; 5: 2014
        • Guizzo Erico
        How google’s self-driving car works.
        IEEE Spectrum Online. 2011; 18: 1132-1141
        • Kessler Aaron M.
        Elon Musk Says Self-Driving Tesla Cars will be in the us by Summer.
        The New York Times, 2015: B1
        • Hurley John S.
        Beyond the struggle: artificial intelligence in the department of defense (DoD).
        ICCWS 2018 13th International Conference on Cyber Warfare and Security. 2018: 297
        • Graesser Arthur
        • Chipman Patrick
        • Leeming Frank
        • Biedenbach Suzanne
        Deep learning and emotion in serious games.
        Serious Games: Mech. Effects. 2009; : 81-100
        • Min Seonwoo
        • Lee Byunghan
        • Yoon Sungroh
        Deep learning in bioinformatics.
        Brief. Bioinf. 2017; 18: 851-869
        • Panda Mrutyunjaya
        Deep learning in bioinformatics.
        CSI Commun. 2012; 4
        • Park Yongjin
        • Kellis Manolis
        Deep learning for regulatory genomics.
        Nat. Biotechnol. 2015; 33: 825
        • Ravì Daniele
        • Wong Charence
        • Deligianni Fani
        • Berthelot Melissa
        • Andreu-Perez Javier
        • Lo Benny
        • Yang Guang-Zhong
        Deep learning for health informatics.
        IEEE J. Biomed. Health Inform. 2017; 21: 4-21
        • Abadi Martín
        • Barham Paul
        • Chen Jianmin
        • Chen Zhifeng
        • Davis Andy
        • Dean Jeffrey
        • Devin Matthieu
        • et al.
        TensorFlow: A System for Large-Scale Machine Learning. 16. OSDI, 2016: 265-283
        • Bergstra James
        • Breuleux Olivier
        • Bastien Frédéric
        • Lamblin Pascal
        • Pascanu Razvan
        • Desjardins Guillaume
        • Turian Joseph
        • Warde-Farley David
        • Bengio Yoshua
        Theano: a CPU and GPU math compiler in python.
        Proc. 9th Python in Science Conf. 2010; 1
        • Jia Yangqing
        • Shelhamer Evan
        • Donahue Jeff
        • Karayev Sergey
        • Long Jonathan
        • Girshick Ross
        • Guadarrama Sergio
        • Darrell Trevor
        Caffe: convolutional architecture for fast feature embedding.
        Proceedings of the 22nd ACM International Conference on Multimedia. 2014; : 675-678
        • Luebke David
        • Humphreys Greg
        How gpus work.
        Computer. 2007; 40
        • Fialka Ondirej
        • Cadik Martin
        FFT and convolution performance in image filtering on GPU.
        Information Visualization, 2006. IV 2006, Tenth International Conference on. 2006; : 609-614
        • Anthimopoulos Marios
        • Christodoulidis Stergios
        • Ebner Lukas
        • Christe Andreas
        • Mougiakakou Stavroula
        Lung pattern classification for interstitial lung diseases using a deep convolutional neural network.
        IEEE Trans. Med. Imaging. 2016; 35: 1207-1216
        • Fakoor Rasool
        • Ladhak Faisal
        • Nazi Azade
        • Huber Manfred
        Using deep learning to enhance cancer diagnosis and classification.
        Proceedings of the International Conference on Machine Learning. 2013; 28
        • Li Rongjian
        • Zhang Wenlu
        • Suk Heung-Il
        • Wang Li
        • Li Jiang
        • Shen Dinggang
        • Ji Shuiwang
        Deep learning based imaging data completion for improved brain disease diagnosis.
        International Conference on Medical Image Computing and Computer-Assisted Intervention. 2014; : 305-312
        • Brosch Tom
        • Tam Roger
        Alzheimer’s disease neuroimaging initiative. Manifold learning of brain MRIs by deep learning.
        International Conference on Medical Image Computing and Computer-Assisted Intervention. 2013; : 633-640
        • Brosch Tom
        • Yoo Youngjin
        • Li David K.B.
        • Traboulsee Anthony
        • Tam Roger
        Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning.
        International Conference on Medical Image Computing and Computer-Assisted Intervention. 2014; : 462-469
        • Zhen Xiantong
        • Wang Zhijie
        • Islam Ali
        • Bhaduri Mousumi
        • Chan Ian
        • Li Shuo
        Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation.
        Med. Image Anal. 2016; 30: 120-129
        • Geremia Ezequiel
        • Menze Bjoern H.
        • Clatz Olivier
        • Konukoglu Ender
        • Criminisi Antonio
        • Ayache Nicholas
        Spatial decision forests for MS lesion segmentation in multi-channel MR images.
        International Conference on Medical Image Computing and Computer-Assisted Intervention. 2010; : 111-118
        • Wang Li
        • Gao Yaozong
        • Shi Feng
        • Li Gang
        • Gilmore John H.
        • Lin Weili
        • Shen Dinggang
        LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images.
        NeuroImage. 2015; 108: 160-172
        • Lynch Michael
        • Ghita Ovidiu
        • Whelan Paul F.
        Automatic segmentation of the left ventricle cavity and myocardium in MRI data.
        Comput. Biol. Med. 2006; 36: 389-407
        • Wachinger Christian
        • Reuter Martin
        • Klein Tassilo
        DeepNAT: deep convolutional neural network for segmenting neuroanatomy.
        NeuroImage. 2017;
        • Wolterink Jelmer M.
        • Leiner Tim
        • Max A.
        • Viergever Ivana I.šgum
        Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease.
        Reconstruction, Segmentation, and Analysis of Medical Images. 2016; : 95-102
        • Avendi M.R.
        • Kheradvar Arash
        • Jafarkhani Hamid
        A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.
        Med. Image Anal. 2016; 30: 108-119
        • van Grinsven Mark J.J.P.
        • van Ginneken Bram
        • Hoyng Carel B.
        • Theelen Thomas
        • Sánchez Clara I.
        Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images.
        IEEE Trans. Med. Imaging. 2016; 35: 1273-1284
        • Dou Qi
        • Chen Hao
        • Yu Lequan
        • Zhao Lei
        • Qin Jing
        • Wang Defeng
        • Mok Vincent C.T.
        • Shi Lin
        • Heng Pheng-Ann
        Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks.
        IEEE Trans. Med. Imaging. 2016; 35: 1182-1195
        • Cireşan Dan C.
        • Giusti Alessandro
        • Gambardella Luca M.
        • Schmidhuber J.ürgen
        Mitosis detection in breast cancer histology images with deep neural networks.
        International Conference on Medical Image Computing and Computer-Assisted Intervention. 2013; : 411-418
        • Sirinukunwattana Korsuk
        • Ahmed Raza Shan E.
        • Tsang Yee-Wah
        • Snead David R.J.
        • Cree Ian A.
        • Rajpoot Nasir M.
        Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images.
        IEEE Trans. Med. Imaging. 2016; 35: 1196-1206
        • Wang Haibo
        • Roa Angel Cruz
        • Basavanhally Ajay N.
        • Gilmore Hannah L.
        • Shih Natalie
        • Feldman Mike
        • Tomaszewski John
        • Gonzalez Fabio
        • Madabhushi Anant
        Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.
        J. Med. Imaging. 2014; 1034003
        • Cruz-Roa Angel
        • Basavanhally Ajay
        • González Fabio
        • Gilmore Hannah
        • Feldman Michael
        • Ganesan Shridar
        • Shih Natalie
        • Tomaszewski John
        • Madabhushi Anant
        Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks, in Medical Imaging 2014: digital pathology.
        Int. Soc. Opt. Photon. 2014; 9041904103
        • Roth Holger R.
        • Le Lu Ari Seff
        • Cherry Kevin M.
        • Hoffman Joanne
        • Wang Shijun
        • Liu Jiamin
        • Turkbey Evrim
        • Summers Ronald M.
        A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations.
        International Conference on Medical Image Computing and Computer-Assisted Intervention. 2014; : 520-527
        • Wang Dayong
        • Khosla Aditya
        • Gargeya Rishab
        • Irshad Humayun
        • Beck Andrew H.
        Deep Learning for Identifying Metastatic Breast Cancer.
        arXiv preprint arXiv:1606.05718, 2016
      3. IBM Research Accelerating Discovery: Medical Image Analytics, 10/10/2013. Available Online. https://www.youtube.com/watch?v=0i11VCNacAE.

        • Bos L.
        Economic impact of telemedicine: a survey.
        Med. Care Compunet. 2005; 2: 140
      4. Available Online. https://www.wired.com/2017/01/look-x-rays-moles-living-ai-coming-job/.

      5. Computer Aided Detection Market Worth $1.9 Billion By 2022,” Grand View Research, 8/2016, Available Online. http://www.grandviewresearch.com/press-release/global-computer-aided-detection-market.

        • Liew Charlene
        The future of radiology augmented with Artificial Intelligence: a strategy for success.
        Eur. J. Radiol. 2018; 102: 152-156
        • Nagar Yiftach
        • Malone Thomas W.
        • Boer Patrick De
        • Garcia Ana Cristina Bicharra
        Essays on Collective Intelligence.
        (PhD diss) Massachusetts Institute of Technology, 2016
        • Kremer Michael
        The O-ring theory of economic development.
        Q. J. Econ. 1993; 108: 551-575
      6. Available Online, https://www.enlitic.com/press-release-10272015.html.

      7. Robert E. Cooke Jr., Michael G. Gaeta, Dean M. Kaufman, John G. Henrici, Picture Archiving and Communication System, U.S. Patent 6,574,629, issued June 3, (2003).