Deep Learning: A Breakthrough in Medical Imaging

Author(s): Hafiz Mughees Ahmad*, Muhammad Jaleed Khan, Adeel Yousaf, Sajid Ghuffar, Khurram Khurshid

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 8 , 2020


Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.

Keywords: Classification, deep learning, detection, medical image analysis, segmentation, retrieval, registration.

[1]
Scatliff JH, Morris PJ. From Roentgen to magnetic resonance imaging: the history of medical imaging. N C Med J 2014; 75(2): 111-3.
[http://dx.doi.org/10.18043/ncm.75.2.111 ] [PMID: 24663131]
[2]
Bui-Mansfield LT, Sutcliffe JB. Nobel Prize laureates who have made significant contributions to radiology. J Comput Assist Tomogr 2009; 33(4): 483-8.
[http://dx.doi.org/10.1097/RCT.0b013e31818dda6e ] [PMID: 19638837]
[3]
McCarthy J, Feigenbaum EA. In Memoriam: Arthur Samuel: Pioneer in Machine Learning. AI Mag 1990; 11: 10-0.
[http://dx.doi.org/10.1609/AIMAG.V11I3.840]
[4]
Aksac A, Demetrick DJ, Ozyer T, Alhajj R. BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis. BMC Res Notes 2019; 12(1): 82.
[http://dx.doi.org/10.1186/s13104-019-4121-7 ] [PMID: 30755250]
[5]
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. Imagenet large scale visual recognition challenge. Int J Comput Vis 2015; 115: 211-52.
[http://dx.doi.org/10.1007/s11263-015-0816-y]
[6]
Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, et al. Microsoft coco: Common objects in context Eur Conf Comput Vis. Springer 2014; pp. 740-55.
[7]
Taigman Y, Marc’ MY, Ranzato A, Wolf L. DeepFace: Closing the Gap to Human-Level Performance in Face Verification n.d.
[8]
Salvador A, Drozdzal M, Giro-i-Nieto X, Romero A. Inverse cooking: Recipe generation from food images. Proc IEEE Conf Comput Vis Pattern Recognit. 10453-62.
[9]
Silver D, Huang A, Maddison CJ, et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016; 529(7587): 484-9.
[http://dx.doi.org/10.1038/nature16961 ] [PMID: 26819042]
[10]
Sun Y, Liang D, Wang X, Tang X. DeepID3: Face Recognition with Very Deep Neural Networks 2015.
[11]
Lee K, Zung J, Li P, Jain V, Seung HS. Superhuman Accuracy on the SNEMI3D Connectomics Challenge 2017.
[12]
He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition 2015.
[13]
Loquercio A, Maqueda AI, Del-Blanco CR, Scaramuzza D. DroNet: Learning to Fly by Driving. IEEE Robot Autom Lett 2018; 1088-95.
[http://dx.doi.org/10.1109/LRA.2018.2795643]
[14]
Shirer M. Worldwide Spending on Cognitive and Artificial Intelligence Systems. Int Data Corp 2019.
[15]
Shirer M. Worldwide Spending on Cognitive and Artificial Intelligence Systems. Int Data Corp 2018.
[16]
Schwab K. The Fourth Industrial Revolution: what it means, how to respond. 1
[17]
Rosen PP. Rosen’s breast pathology. Lippincott Williams & Wilkins 2001.
[18]
Tang J, Rangayyan RM, Xu J, El Naqa I, Yang Y. Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 2009; 13(2): 236-51.
[http://dx.doi.org/10.1109/TITB.2008.2009441 ] [PMID: 19171527]
[19]
Elgamal EA. Natural history of hydrocephalus in children with spinal open neural tube defect. Surg Neurol Int 2012; 3: 112.
[http://dx.doi.org/10.4103/2152-7806.101801 ] [PMID: 23087828]
[20]
Gupta S, Mehendiratta M, Rehani S, Kumra M, Nagpal R, Gupta R. Age estimation in Indian children and adolescents in the NCR region of Haryana: A comparative study. J Forensic Dent Sci 2015; 7(3): 253-8.
[http://dx.doi.org/10.4103/0975-1475.172453 ] [PMID: 26814053]
[21]
Salehinejad H, Valaee S, Dowdell T, Colak E, Barfett J. Generalization of Deep Neural Networks for Chest Pathology Classification in X-Rays Using Generative Adversarial Networks IEEE Int Conf Acoust Speech Signal Process ICASSP. 990-4. http://https://doi.org/10.1109/ICASSP.2018.8461430
[22]
Araújo T, Aresta G, Castro E, et al. Classification of breast cancer histology images using Convolutional Neural Networks. PLoS One 2017; 12(6)e0177544
[http://dx.doi.org/10.1371/journal.pone.0177544 ] [PMID: 28570557]
[23]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015.
[24]
Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw 2015; 61: 85-117.
[http://dx.doi.org/10.1016/j.neunet.2014.09.003 ] [PMID: 25462637]
[25]
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. J Mach Learn Res 2011; 12: 2493-537.
[26]
Sainath TN, Mohamed A, Kingsbury B, Ramabhadran B. Deep convolutional neural networks for LVCSR IEEE Int Conf Acoust Speech Signal Process. 8614-8.
[27]
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2(4): 230-43.
[http://dx.doi.org/10.1136/svn-2017-000101 ] [PMID: 29507784]
[28]
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Cham: Springer 2015; pp. 234-41.
[29]
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput 1989; 1: 541-51.
[http://dx.doi.org/10.1162/neco.1989.1.4.541]
[30]
LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998; 86: 2278-324.
[http://dx.doi.org/10.1109/5.726791]
[31]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012; 1097-105.
[32]
Rosenblatt F. The perceptron: a probabilistic model for informationstorage and organization in the brain. Psychol Rev 1958; 65(6): 386-408.
[http://dx.doi.org/10.1037/h0042519 ] [PMID: 13602029]
[33]
Yao X. Evolving artificial neural networks. Proc IEEE 1999; 87: 1423-47.
[http://dx.doi.org/10.1109/5.784219]
[34]
Hassoun MH. Fundamentals of artificial neural networks. MIT press 1995.
[35]
Jain AK, Mao J, Mohiuddin KM. Artificial neural networks: A tutorial. Computer 1996; 29(3): 31-44.
[http://dx.doi.org/10.1109/2.485891]
[36]
Khan J, Wei JS, Ringnér M, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001; 7(6): 673-9.
[http://dx.doi.org/10.1038/89044 ] [PMID: 11385503]
[37]
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A Survey on Deep Learning in Medical Image Analysis 2017.
[http://dx.doi.org/10.1016/j.media.2017.07.005]
[38]
Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A. Modern Trends in Hyperspectral Image Analysis: A Review
[http://dx.doi.org/10.1109/ACCESS.2018.2812999]
[39]
Khan MJ, Yousaf A, Abbas A, Khurshid K. Deep learning for automated forgery detection in hyperspectral document images. J Electron Imaging 2018; 27: 1.
[http://dx.doi.org/10.1117/1.JEI.27.5.053001]
[40]
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition 2014.
[41]
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going Deeper with Convolutions 2014.
[42]
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-First AAAI Conf Artif Intell.
[43]
Sutskever I, Martens J, Hinton GE. Generating text with recurrent neural networks. Proc. 28th Int. Conf. Mach Learn 2011; ICML-11: 1017-24.
[44]
Graves A. Generating sequences with recurrent neural networks.ArXiv Prepr ArXiv13080850 2013 2013.
[45]
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997; 9(8): 1735-80.
[http://dx.doi.org/10.1162/neco.1997.9.8.1735 ] [PMID: 9377276]
[46]
Choi K, Fazekas G, Sandler M. Text-based LSTM networks for automatic music composition 2016.
[47]
Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation 2014.
[http://dx.doi.org/10.3115/v1/D14-1179]
[48]
Chen J, Yang L, Zhang Y, Alber M, Chen DZ. Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation. Adv Neural Inf Process Syst 2016; 3036-44.
[49]
Stollenga MF, Byeon W, Liwicki M, Schmidhuber J. Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation. Adv Neural Inf Process Syst 2015; 2998-3006.
[50]
Shin H-C, Roberts K, Lu L, Demner-Fushman D, Yao J, Summers RM. Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation Proc IEEE Conf Comput Vis Pattern Recognit. 2497-506.
[51]
Khan MJ, Yousaf A, Khurshid K, Abbas A, Shafait F. Automatd Forgery Detection in Multispectral Document Images using Fuzzy Clustering. 13th IAPR Int Workshop Doc Anal Syst.
[http://dx.doi.org/10.1109/DAS.2018.26]
[52]
Kingma DP, Welling M. Auto-encoding variational bayes 2013.
[53]
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. J Mach Learn Res 2010; 11: 3371-408.
[54]
Poultney C, Chopra S, Cun YL. Efficient learning of sparse representations with an energy-based model. Adv Neural Inf Process Syst 2007; 1137-44.
[55]
Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput 2006; 18(7): 1527-54.
[http://dx.doi.org/10.1162/neco.2006.18.7.1527 ] [PMID: 16764513]
[56]
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative Adversarial Networks 2014.
[57]
Kazeminia S, Baur C, Kuijper A, van Ginneken B, Navab N, Albarqouni S, et al. GANs for Medical Image Analysis. 2018. ArXiv180906222 Cs Stat 2018
[58]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436-44.
[http://dx.doi.org/10.1038/nature14539 ] [PMID: 26017442]
[59]
Khan MJ, Yousaf A, Javed N, Nadeem S, Khurshid K. Automatic Target Detection in Satellite Images using Deep Learning. J Space Technol 2017; 7: 44-9.
[60]
Kim E, Corte-Real M, Baloch Z. A deep semantic mobile application for thyroid cytopathology. International Society for Optics and Photonics 2016; Vol. 9789p. 97890A
[61]
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115-8.
[http://dx.doi.org/10.1038/nature21056 ] [PMID: 28117445]
[62]
Plis SM, Hjelm DR, Salakhutdinov R, et al. Deep learning for neuroimaging: a validation study. Front Neurosci 2014; 8: 229.
[http://dx.doi.org/10.3389/fnins.2014.00229 ] [PMID: 25191215]
[63]
Suk H-I, Shen D. Deep Learning-Based Feature Representation for AD/MCI Classification Int Conf Med Image Comput Comput- Assist Interv,. 583-90.
[64]
Hosseini-Asl E, Gimel’farb G, El-Baz A. Alzheimer’s Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network 2016.
[65]
Payan A, Montana G. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks 2015.
[66]
Charan S, Khan MJ, Khurshid K. Breast cancer detection in mammograms using convolutional neural network Int Conf Comput Math Eng Technol. 1-5. http://https://doi.org/10.1109/ICOMET.2018.8346384
[67]
Menegola A, Fornaciali M, Pires R, Avila S, Valle E. Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes 2016.
[68]
Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y. Convolutional Neural Networks for Diabetic Retinopathy. Procedia Comput Sci 2016; 90: 200-5.
[http://dx.doi.org/10.1016/j.procs.2016.07.014]
[69]
de Vos BD, Wolterink JM, de Jong PA, Viergever MA, Išgum I. 2D image classification for 3D anatomy localization: employing deep convolutional neural networks. Styner: MA 2016; p. 97841Y.
[70]
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; 51: 11-9.
[http://dx.doi.org/10.1016/j.compmedimag.2016.02.002 ] [PMID: 27104497]
[71]
Kumar A, Sridar P, Quinton A, Kumar RK, Feng D, Nanan R, et al. Plane identification in fetal ultrasound images using saliency maps and convolutional neural networks IEEE 13th Int Symp Biomed Imaging ISBI. 791-4. http://https://doi.org/10.1109/ISBI.2016.7493385
[72]
Ghesu FC, Georgescu B, Mansi T, Neumann D, Hornegger J, Comaniciu D. An Artificial Agent for Anatomical Landmark Detection in Medical Images.Int Conf Med Image Comput Comput- Assist Interv. 229-37.
[http://dx.doi.org/10.1007/978-3-319-46726-9_27]
[73]
Kong B, Zhan Y, Shin M, Denny T, Zhang S. Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network.Int Conf Med Image Comput Comput-Assist Interv 2016; 264-72.
[http://dx.doi.org/10.1007/978-3-319-46726-9_31]
[74]
Liao F, Liang M, Li Z, Hu X, Song S. Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network. 2017. Add DOI
[http://dx.doi.org/10.1109/TNNLS.2019.2892409]
[75]
Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. Int Conf Med Image Comput Comput-Assist Interv 2013; pp. 411-8.
[http://dx.doi.org/10.1007/978-3-642-40763-5_51]
[76]
Yang X, Yeo S-Y, Hong JM, Wong ST, Tang WT, Wu ZZ, et al. A Deep Learning Approach for Tumor Tissue Image Classification Biomed Eng. Calgary, AB, Canada: ACTAPRESS 2016.
[77]
Falk T, Mai D, Bensch R, et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods 2019; 16(1): 67-70.
[http://dx.doi.org/10.1038/s41592-018-0261-2 ] [PMID: 30559429]
[78]
Zhang Z, Liu Q, Wang Y. Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett 2018; 15: 749-53.
[http://dx.doi.org/10.1109/LGRS.2018.2802944]
[79]
Xie Y, Zhang Z, Sapkota M, Yang L. Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation. Int Conf Med Image Comput Comput-Assist Interv. 185-93.
[http://dx.doi.org/10.1007/978-3-319-46723-8_22]
[80]
Andermatt S, Pezold S, Cattin P. Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data. Proceeding Deep Learn. Med. Image Anal. DLMIA Lect. Notes Comput. Sci 2016; pp. 142-51.
[http://dx.doi.org/10.1007/978-3-319-46976-8_15]
[81]
Brosch T, Tang LYW. Youngjin Yoo, Li DKB, Traboulsee A, Tam R. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. IEEE Trans Med Imaging 2016; 35(5): 1229-39.
[http://dx.doi.org/10.1109/TMI.2016.2528821 ] [PMID: 26886978]
[82]
Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging 2017; 30(4): 449-59.
[http://dx.doi.org/10.1007/s10278-017-9983-4 ] [PMID: 28577131]
[83]
Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJNL, Isgum I. Automatic Segmentation of MR Brain Images With a Convolutional Neural Network. IEEE Trans Med Imaging 2016; 35(5): 1252-61.
[http://dx.doi.org/10.1109/TMI.2016.2548501 ] [PMID: 27046893]
[84]
Kohl S, Bonekamp D, Schlemmer H-P, Yaqubi K, Hohenfellner M, Hadaschik B, et al. Adversarial Networks for the Detection of Aggressive Prostate Cancer. . ArXiv170208014 Cs 2017 2017.
[85]
Cheng X, Zhang L, Zheng Y. Deep similarity learning for multimodal medical images. Comput Methods Biomech Biomed Eng Imaging Vis 2018; 6: 248-52.
[http://dx.doi.org/10.1080/21681163.2015.1135299]
[86]
Simonovsky M, Gutiérrez-Becker B, Mateus D, Navab N, Komodakis N. A Deep Metric for Multimodal Registration
[http://dx.doi.org/10.1007/978-3-319-46726-9_2]
[87]
Shun Miao, Wang ZJ, Rui Liao. A CNN Regression Approach for Real-Time 2D/3D Registration. IEEE Trans Med Imaging 2016; 35(5): 1352-63.
[http://dx.doi.org/10.1109/TMI.2016.2521800 ] [PMID: 26829785]
[88]
Anavi Y, Kogan I, Gelbart E, Geva O, Greenspan H. Visualizing and enhancing a deep learning framework using patients age and gender for chest x-ray image retrieval
[89]
Shah A, Conjeti S, Navab N, Katouzian A. Deeply learnt hashing forests for content based image retrieval in prostate MR images.International Society for Optics and Photonics. Styner, MA 2016; Vol. 9784: :p. 978414.
[90]
Tsochatzidis L, Zagoris K, Arikidis N, Karahaliou A, Costaridou L, Pratikakis I. Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recognit 2017; 71: 106-17.
[http://dx.doi.org/10.1016/j.patcog.2017.05.023]
[91]
Gonzalez RT, Riascos JA, Barone DAC. How Artificial Intelligence is Supporting Neuroscience Research: A Discussion About Foundations, Methods and Applications. Cham: Springer 2017; pp. 63-77.
[92]
Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 2005; 18(5-6): 602-10.
[http://dx.doi.org/10.1016/j.neunet.2005.06.042 ] [PMID: 16112549]
[93]
Lev G, Sadeh G, Klein B, Wolf L. RNN Fisher Vectors for Action Recognition and Image Annotation. Cham: Springer 2016; pp. 833-50.
[http://dx.doi.org/10.1007/978-3-319-46466-4_50]
[94]
Cai J, Lu L, Xie Y, Xing F, Yang L. Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks. Cham: Springer 2017; pp. 674-82.
[http://dx.doi.org/10.1007/978-3-319-66179-7_77]
[95]
Suk H-I, Wee C-Y, Lee S-W, Shen D. State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage 2016; 129: 292-307.
[http://dx.doi.org/10.1016/j.neuroimage.2016.01.005 ] [PMID: 26774612]
[96]
Talathi SS. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems 2017.
[http://dx.doi.org/10.2172/1366924]
[97]
Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 2017; 155: 530-48.
[http://dx.doi.org/10.1016/j.neuroimage.2017.03.057 ] [PMID: 28414186]
[98]
McCann MT, Jin KH, Unser M. Convolutional Neural Networks for Inverse Problems in Imaging: A Review. IEEE Signal Process Mag 2017; 34: 85-95.
[http://dx.doi.org/10.1109/MSP.2017.2739299]
[99]
Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2018; 2(3): 158-64.
[http://dx.doi.org/10.1038/s41551-018-0195-0 ] [PMID: 31015713]
[100]
Ghafoorian M, Karssemeijer N, Heskes T, et al. Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. Sci Rep 2017; 7(1): 5110.
[http://dx.doi.org/10.1038/s41598-017-05300-5 ] [PMID: 28698556]
[101]
Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 2013; 500(7461): 168-74.
[http://dx.doi.org/10.1038/nature12346 ] [PMID: 23925239]
[102]
Funke J, Tschopp FD, Grisaitis W, Sheridan A, Singh C, Saalfeld S, et al. A Deep Structured Learning Approach Towards Automating Connectome Reconstruction from 3D Electron Micrographs 2017.
[103]
Turaga SC, Murray JF, Jain V, et al. Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput 2010; 22(2): 511-38.
[http://dx.doi.org/10.1162/neco.2009.10-08-881 ] [PMID: 19922289]
[104]
Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J 2010; 31: 2369-429.
[105]
Rajpurkar P, Hannun AY, Haghpanahi M, Bourn C, Ng AY. Cardiologist-level arrhythmia detection with convolutional neural networks.. ArXiv Prepr ArXiv170701836
[106]
Zihlmann M, Perekrestenko D, Tschannen M. Convolutional recurrent neural networks for electrocardiogram classification 2017 Comput Cardiol CinC. IEEE 2017; pp. 1-4.
[107]
Database P. No Title nd https://physionet.org/challenge/2017/
[108]
Andreotti F, Carr O, Pimentel MAF, Mahdi A, De Vos M. Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG 2017 Comput Cardiol CinC. IEEE 2017; pp. 1-4.
[109]
Ghiasi S, Abdollahpur M, Madani N, Kiani K, Ghaffari A. Atrial fibrillation detection using feature based algorithm and deep convolutional neural network 2017 Comput Cardiol CinC. IEEE 2017; pp. 1-4.
[110]
Limam M, Precioso F. Atrial fibrillation detection and ECG classification based on convolutional recurrent neural network 2017 Comput Cardiol CinC. IEEE 2017; pp. 1-4.
[111]
Rubin J, Parvaneh S, Rahman A, Conroy B, Babaeizadeh S. Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ECG recordings 2017 Comput Cardiol CinC. IEEE 2017; pp. 1-4.
[112]
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. CVPR 2017; 1: 3.
[113]
Xiong Z, Stiles MK, Zhao J. Robust ECG signal classification for detection of atrial fibrillation using a novel neural network 2017 Comput Cardiol CinC. IEEE 2017; pp. 1-4.
[114]
Hong S, Wu M, Zhou Y, Wang Q, Shang J, Li H, et al. ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks 2017 Comput Cardiol CinC. IEEE 2017; pp. 1-4.
[115]
Schwab P, Scebba GC, Zhang J, Delai M, Karlen W. Beat by beat: Classifying cardiac arrhythmias with recurrent neural networks 2017 Comput Cardiol CinC. IEEE 2017; pp. 1-4.
[116]
Soochahn Lee, Lee S, Yun ID, Kim SM, Lee KM. Seung Yeon Shin; Il Dong Yun; Sun Mi Kim; Kyoung Mu Lee. Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images. IEEE Trans Med Imaging 2019; 38(3): 762-74.
[http://dx.doi.org/10.1109/TMI.2018.2872031 ] [PMID: 30273145]
[117]
Yousefi M, Krzyżak A, Suen CY. Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. Comput Biol Med 2018; 96: 283-93.
[http://dx.doi.org/10.1016/j.compbiomed.2018.04.004 ] [PMID: 29665537]
[118]
Kim DH, Kim ST, Ro YM. atent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis IEEE Int Conf Acoust Speech Signal Process. 927-31.
[119]
Aresta G, Araújo T, Kwok S, et al. BACH: Grand challenge on breast cancer histology images. Med Image Anal 2019; 56: 122-39.
[http://dx.doi.org/10.1016/j.media.2019.05.010 ] [PMID: 31226662]
[120]
Kooi T, Litjens G, van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 2017; 35: 303-12.
[http://dx.doi.org/10.1016/j.media.2016.07.007 ] [PMID: 27497072]
[121]
Patterson SK, Roubidoux MA. Update on new technologies in digital mammography. Int J Womens Health 2014; 6: 781-8.
[http://dx.doi.org/10.2147/IJWH.S49332 ] [PMID: 25152634]
[122]
Becker AS, Mueller M, Stoffel E, Marcon M, Ghafoor S, Boss A. Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. Br J Radiol 2018; 91(1083) 20170576
[PMID: 29215311]
[123]
Levman J, Leung T, Causer P, Plewes D, Martel AL. Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines. IEEE Trans Med Imaging 2008; 27(5): 688-96.
[http://dx.doi.org/10.1109/TMI.2008.916959 ] [PMID: 18450541]
[124]
Leach MO, Boggis CR, Dixon AK, et al. MARIBS study group. Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS). Lancet 2005; 365(9473): 1769-78.
[http://dx.doi.org/10.1016/S0140-6736(05)66481-1 ] [PMID: 15910949]
[125]
Baker JA, Lo JY. Breast tomosynthesis: state-of-the-art and review of the literature. Acad Radiol 2011; 18(10): 1298-310.
[http://dx.doi.org/10.1016/j.acra.2011.06.011 ] [PMID: 21893296]
[126]
Samala RK, Chan H-P, Hadjiiski L, Helvie MA, Wei J, Cha K. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. Med Phys 2016; 43(12): 6654-66.
[http://dx.doi.org/10.1118/1.4967345 ] [PMID: 27908154]
[127]
Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N. Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 2016; 35(5): 1313-21.
[http://dx.doi.org/10.1109/TMI.2016.2528120 ] [PMID: 26891484]
[128]
Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer 2016.
[129]
Ahmad HM, Ghuffar S, Khurshid K. Classification of Breast Cancer Histology Images Using Transfer Learning 16th Int Bhurban Conf Appl Sci Technol. 328-332.
[130]
Chen H, Zhang K, Lyu P, et al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Sci Rep 2019; 9(1): 3840.
[http://dx.doi.org/10.1038/s41598-019-40414-y ] [PMID: 30846758]
[131]
Krois J, Ekert T, Meinhold L, et al. Deep Learning for the Radiographic Detection of Periodontal Bone Loss. Sci Rep 2019; 9(1): 8495.
[http://dx.doi.org/10.1038/s41598-019-44839-3 ] [PMID: 31186466]
[132]
Ronneberger O, Fischer P, Brox T. Dental X-ray image segmentation using a U-shaped Deep Convolutional network. ISBI 2015.
[133]
Wang C-W, Huang C-T, Lee J-H, et al. A benchmark for comparison of dental radiography analysis algorithms. Med Image Anal 2016; 31: 63-76.
[http://dx.doi.org/10.1016/j.media.2016.02.004 ] [PMID: 26974042]
[134]
Chu P, Bo C, Liang X, Yang J, Megalooikonomou V, Yang F, et al. Using octuplet siamese network for osteoporosis analysis on dental panoramic radiograph 40th Annu Int Conf IEEE Eng Med Biol Soc. 2579-82.
[135]
Lee J-S, Adhikari S, Liu L, Jeong H-G, Kim H, Yoon S-J. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofac Radiol 2018.4820170344
[http://dx.doi.org/10.1259/dmfr.20170344 ] [PMID: 30004241]
[136]
Egger J, Pfarrkirchner B, Gsaxner C, Lindner L, Schmalstieg D, Wallner J. Fully Convolutional Mandible Segmentation on a valid Ground- Truth Dataset Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf . 656-0.
[137]
Marcu LG, Boyd C, Bezak E. Current issues regarding artificial intelligence in cancer and health care. Implications for medical physicists and biomedical engineers. Health Technol 2019; 9: 375-81.
[http://dx.doi.org/10.1007/s12553-019-00348-x]
[138]
Paulson SS, Scruth E. Legal and Ethical Concerns of Big Data: Predictive Analytics. Clin Nurse Spec 2017; 31(5): 237-9.
[http://dx.doi.org/10.1097/NUR.0000000000000315 ] [PMID: 28806228]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 16
ISSUE: 8
Year: 2020
Published on: 18 December, 2019
Page: [946 - 956]
Pages: 11
DOI: 10.2174/1573405615666191219100824
Price: $65

Article Metrics

PDF: 30
HTML: 3