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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Review Article

A Tour of Unsupervised Deep Learning for Medical Image Analysis

Author(s): Khalid Raza* and Nripendra Kumar Singh

Volume 17, Issue 9, 2021

Published on: 27 January, 2021

Page: [1059 - 1077] Pages: 19

DOI: 10.2174/1573405617666210127154257

Price: $65

Abstract

Background: Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical image analysis. Several reviews on supervised deep learning are published, but hardly any rigorous review on unsupervised deep learning for medical image analysis is available.

Objective: The objective of this review is to systematically present various unsupervised deep learning models, tools, and benchmark datasets applied to medical image analysis. Some of the discussed models are autoencoders and their variants, Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), Deep Boltzmann Machine (DBM), and Generative Adversarial Network (GAN). Future research opportunities and challenges of unsupervised deep learning techniques for medical image analysis are also discussed.

Conclusion: Currently, interpretation of medical images for diagnostic purposes is usually performed by human experts that may be replaced by computer-aided diagnosis due to advancement in machine learning techniques, including deep learning, and the availability of cheap computing infrastructure through cloud computing. Both supervised and unsupervised machine learning approaches are widely applied in medical image analysis, each of them having certain pros and cons. Since human supervisions are not always available or are inadequate or biased, therefore, unsupervised learning algorithms give a big hope with lots of advantages for biomedical image analysis.

Keywords: Unsupervised learning, medical image analysis, autoencoders, restricted boltzmann machine, deep belief network, MRI.

Graphical Abstract

[1]
Wani N, Raza K. Multiple kernel learning approach for medical image analysis. Soft Computing Based Medical Image Analysis 2018; 31-47.
[http://dx.doi.org/10.1016/B978-0-12-813087-2.00002-6]
[2]
Jabeen A, Ahmad N, Raza K. machine learning-based state-of-the-art methods for the classification of RNA-seq data. Classification in BioApps 2018; 6: 133-72.
[http://dx.doi.org/10.1007/978-3-319-65981-7_6]
[3]
Bourlard H, Kamp Y. Auto-association by multilayer perceptrons and singular value decomposition. Biol Cybern 1988; 59(4-5): 291-4.
[http://dx.doi.org/10.1007/BF00332918] [PMID: 3196773]
[4]
Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 2013; 35(8): 1798-828.
[http://dx.doi.org/10.1109/TPAMI.2013.50] [PMID: 23787338]
[5]
Shin HC, Orton MR, Collins DJ, Doran SJ, Leach MO. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans Pattern Anal Mach Intell 2013; 35(8): 1930-43.
[http://dx.doi.org/10.1109/TPAMI.2012.277] [PMID: 23787345]
[6]
Vincent P, Larochelle H, Lajoie I. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 2010; 11: 3371-408.
[7]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
[8]
Bengio Y, Lamblin P, Popovici D, et al. Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 2006; 19: 153-60.
[9]
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science 2006; 313(5786): 504-7.
[http://dx.doi.org/10.1126/science.1127647] [PMID: 16873662]
[10]
Hinton GE, Osindero S, Teh YW. 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]
[11]
Ng A. Sparse autoencoder lecture notes 2013. Available from: web.stanford.edu/class/cs294a/sparseAutoencoder.pdf
[12]
Makhzani A, Frey B. k-Sparse Autoencoders 2013.
[13]
Li F, Qiao H, Zhang B. Discriminatively boosted image clustering with fully convolutional auto-encoders. Pattern Recognit 2018; 83: 161-73.
[http://dx.doi.org/10.1016/j.patcog.2018.05.019]
[14]
Guo X, Liu X, Zhu E, Yin J. Deep clustering with convolutional autoencoders. International Conference on Neural Information Processing. 373-82.
[15]
Kingma DP, Welling M. Auto-encoding variational bayes. CoRRabs 2013.
[16]
Partaourides H, Chatzis SP. Asymmetric deep generative models. Neurocomputing 2017; 241: 90.
[http://dx.doi.org/10.1016/j.neucom.2017.02.028]
[17]
Ilse M, Tomczak JM, Louizos C, Welling M. Domain invariant variational autoencoders. Medical Imaging with Deep Learning 2020; 322-48.
[18]
Rifai S, Vincent P, Muller X, et al. Contractive auto-encoders: explicit invariance during feature extraction.Proceedings of the 28th International Conference on International Conference on Machine Learning (ICML 2011). 833-40.
[19]
Ballard DH. Modular Learning in Neural Networks. AAAI 1987; pp. 279-84.
[20]
Pinaya WHL, Sandra V, Rafael G-D, et al. Autoencoders machine learning academic press. 2020; 193-208.
[21]
Zabalza J, Ren J, Zheng J, et al. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing 2016; 185: 1-10.
[http://dx.doi.org/10.1016/j.neucom.2015.11.044]
[22]
Goodfellow I, Lee H, Le Q, et al. Measuring invariances in deep networks. Adv Neural Inf Process Syst 2009; 22: 646-54.
[23]
Gallinari P, LeCun Y, Thiria S, et al. Memoires associative distributes. Proceedings of COGNITIVA. 87: Paris.
[24]
Vincent H, Larochelle Y. Extracting and composing robust features with denoising autoencoders. In: Cohen WW, McCallum A, Roweis ST, Eds. Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML’08). 1096-3.
[http://dx.doi.org/10.1145/1390156.1390294]
[25]
Suk H-I, Shen D. Deep learning-based feature representation for AD/MCI classification. Proceedings of the Medical Image Computing and Computer-Assisted Intervention. 8150: 583-90.
[http://dx.doi.org/10.1007/978-3-642-40763-5_72]
[26]
Suk H-I, Lee S-W, Shen D. Alzheimer’s Disease Neuroimaging Initiative. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 2015; 220(2): 841-59.
[http://dx.doi.org/10.1007/s00429-013-0687-3] [PMID: 24363140]
[27]
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]
[28]
Zhu Y, Wang L, Liu M, et al. MRI-based prostate cancer detection with high-level representation and hierarchical classification. Med Phys 2017; 44(3): 1028-39.
[http://dx.doi.org/10.1002/mp.12116] [PMID: 28107548]
[29]
Kallenberg M, Petersen K, Nielsen M, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 2016; 35(5): 1322-31.
[http://dx.doi.org/10.1109/TMI.2016.2532122] [PMID: 26915120]
[30]
Mendoza-Léon R, Puentes J, Uriza LF, Hernández Hoyos M. Single-slice Alzheimer’s disease classification and disease regional analysis with Supervised Switching Autoencoders. Comput Biol Med 2020; 116: 103527.
[http://dx.doi.org/10.1016/j.compbiomed.2019.103527] [PMID: 31765915]
[31]
Dong Q, Qiang N, Lv J, Li X, Liu T, Li Q. Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification. Lect Notes Comput Sci 2020; 2020: 12267.
[http://dx.doi.org/10.1007/978-3-030-59728-3_50]
[32]
Hecht H, Sarhan MH, Popovici V. Disentangled autoencoder for cross-stain feature extraction in pathology image analysis. Appl Sci (Basel) 2020; 10(18): 6427.
[http://dx.doi.org/10.3390/app10186427]
[33]
Dong Q, Qiang N, Lv J, et al. Discovering functional brain networks with 3D residual autoencoder (ResAE). Lect Notes Comput Sci 2020; 12267.
[http://dx.doi.org/10.1007/978-3-030-59728-3_49]
[34]
Adarsh R, Amarnageswarao G, Pandeeswari R, Deivalakshmi S. Dense Residual Convolutional Auto Encoder For Retinal Blood Vessels Segmentation. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). 280-4.
[http://dx.doi.org/10.1109/ICACCS48705.2020.9074172]
[35]
Payan A, Montana G. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv preprin 2015.
[36]
Guo Y, Wu G, Commander L-A, et al. Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features. International Conference on Medical Image Computing and Computer-Assisted Intervention. 308-15.
[http://dx.doi.org/10.1007/978-3-319-10470-6_39]
[37]
Mansoor A, Cerrolaza JJ, Idrees R, et al. Deep learning guided partitioned shape model for anterior visual path- way segmentation. IEEE Trans Med Imaging 2016; 35(8): 1856-65.
[http://dx.doi.org/10.1109/TMI.2016.2535222] [PMID: 26930677]
[38]
Benou A, Veksler R, Friedman A, et al. De-noising of contrast-enhanced MRI sequences by an ensemble of expert deep neural networks. Deep Learning and Data Labeling for Medical Applications. Cham: Springer 2016; pp. 95-110.
[http://dx.doi.org/10.1007/978-3-319-46976-8_11]
[39]
Li D, Fu Z, Xu J. Stacked-autoencoder-based model for COVID-19 diagnosis on CT images. Appl Intell 2021; 51: 2805-17.
[http://dx.doi.org/10.1007/s10489-020-02002-w]
[40]
Xu J, Xiang L, Liu Q, et al. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 2016; 35(1): 119-30.
[http://dx.doi.org/10.1109/TMI.2015.2458702] [PMID: 26208307]
[41]
Janowczyk A, Basavanhally A, Madabhushi A. Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Comput Med Imaging Graph 2017; 57: 50-61.
[http://dx.doi.org/10.1016/j.compmedimag.2016.05.003] [PMID: 27373749]
[42]
Hatipoglu N, Bilgin G. Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput 2017; 55(10): 1829-48.
[http://dx.doi.org/10.1007/s11517-017-1630-1] [PMID: 28247185]
[43]
Avendi MR, Kheradvar A, Jafarkhani H. Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach. Magn Reson Med 2017; 78(6): 2439-48.
[http://dx.doi.org/10.1002/mrm.26631] [PMID: 28205298]
[44]
Su H, Xing F, Kong X, et al. Robust cell detection and segmentation in histopathological images using sparse reconstruction and stacked denoising autoencoders. Lect Notes Comput Sci 2018; 2018: 9351.
[45]
Larrazabal AJ, Martínez C, Glocker B, Ferrante E. Post-dae: Anatomically plausible segmentation via post-processing with denoising autoencoders. IEEE Trans Med Imaging 2020; 39(12): 3813-20.
[http://dx.doi.org/10.1109/TMI.2020.3005297] [PMID: 32746125]
[46]
Liu S, Liu S, Cai W, et al. Early diagnosis of Alzheimer’s disease with deep learning. IEEE Int Symp Biomed Imaging 2014; 1015-8.
[http://dx.doi.org/10.1109/ISBI.2014.6868045]
[47]
Amin J, Sharif M, Gul N, et al. Brain tumor detection by using stacked autoencoders in deep learning. J Med Syst 2019; 44(2): 32.
[http://dx.doi.org/10.1007/s10916-019-1483-2] [PMID: 31848728]
[48]
Cheng J-Z, Ni D, Chou Y-H, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 2016; 6: 24454.
[http://dx.doi.org/10.1038/srep24454] [PMID: 27079888]
[49]
Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 2016; 6: 26094.
[http://dx.doi.org/10.1038/srep26094] [PMID: 27185194]
[50]
Cheng LZ, Zheng Y. Deep similarity learning for multimodal medical images. Comput Methods Biomech Biomed Eng Imaging Vis 2018; 6(3): 248-52.
[http://dx.doi.org/10.1080/21681163.2015.1135299]
[51]
Huang H, Hu X, Zhao Y, et al. Modeling task fMRI data via deep convolutional autoencoder. IEEE Trans Med Imaging 2018; 37(7): 1551-61.
[http://dx.doi.org/10.1109/TMI.2017.2715285] [PMID: 28641247]
[52]
Kazlouski S. Tuberculosis CT image analysis using image features extracted by 3D autoencoder. International Conference of the Cross-Language Evaluation Forum for European Languages. 131-40.
[http://dx.doi.org/10.1007/978-3-030-58219-7_12]
[53]
Hosseini-Asl E, Gimelfarb G, El-Baz A. Alzheimer’s disease diagnostics by a deeply supervised adaptable 3D convolutional network. arxiv 2016.
[54]
Hou L, Nguyen V, Kanevsky AB, et al. Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images. Pattern Recognit 2019; 86: 188-200.
[http://dx.doi.org/10.1016/j.patcog.2018.09.007] [PMID: 30631215]
[55]
Sital C, Brosch T, Tio D, Raaijmakers A, Weese J. 3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images. arXiv preprint 2020.
[56]
Hinton G. A practical guide to training restricted boltzmann machines. Momentum 2010; 9(1): 926.
[57]
Yoo Y, Brosch T, Traboulsee A, et al. Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. International Workshop on Machine Learning in Medical Imaging. 117-24.
[http://dx.doi.org/10.1007/978-3-319-10581-9_15]
[58]
Huang H, Hu X, Han J, et al. Latent source mining in FMRI data via deep neural network. Proceedings of the IEEE Int Symp Biomed Imaging. 638-41.
[http://dx.doi.org/10.1109/ISBI.2016.7493348]
[59]
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]
[60]
Jaumard-Hakoun A, Xu K, Roussel-Ragot P, et al. Tongue contour extraction from ultrasound images based on deep neural network. arxiv 2016.
[61]
Cao P, Liu X, Bao H, Yang J, Zhao D. Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD. Biomed Mater Eng 2015; 26(Suppl. 1): S1541-7.
[http://dx.doi.org/10.3233/BME-151453] [PMID: 26405918]
[62]
Zhang Q, Xiao Y, Dai W, et al. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 2016; 72: 150-7.
[http://dx.doi.org/10.1016/j.ultras.2016.08.004] [PMID: 27529139]
[63]
van Tulder G, de Bruijne M. Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted boltzmann machines. IEEE Trans Med Imaging 2016; 35(5): 1262-72.
[http://dx.doi.org/10.1109/TMI.2016.2526687] [PMID: 26886968]
[64]
Mathews SM, Kambhamettu C, Barner KE. A novel application of deep learning for single-lead ECG classification. Comput Biol Med 2018; 99: 53-62.
[http://dx.doi.org/10.1016/j.compbiomed.2018.05.013] [PMID: 29886261]
[65]
Pereira S, Meier R, McKinley R, et al. Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation. Med Image Anal 2018; 44: 228-44.
[http://dx.doi.org/10.1016/j.media.2017.12.009] [PMID: 29289703]
[66]
Nahid A-A, Mikaelian A, Kong Y. Histopathological breast-image classification with restricted Boltzmann machine along with backpropagation. Biomed Res (Aligarh) 2018; 29(10): 2068-77.
[67]
Lu N, Li T, Ren X, Miao H. A Deep learning scheme for motor imagery classification based on restricted Boltzmann machines. IEEE Trans Neural Syst Rehabil Eng 2017; 25(6): 566-76.
[http://dx.doi.org/10.1109/TNSRE.2016.2601240] [PMID: 27542114]
[68]
Bengio Y. Learning deep architectures for AI. Found Trends Mach Learn 2019; 2(1): 1-127.
[http://dx.doi.org/10.1561/2200000006]
[69]
Hinton GE, Dayan P, Frey BJ, Neal RM. The “wake-sleep” algorithm for unsupervised neural networks. Science 1995; 268(5214): 1158-61.
[http://dx.doi.org/10.1126/science.7761831] [PMID: 7761831]
[70]
Lee H, Grosse R, Ranganath R, et al. Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun ACM 2011; 54(10): 95-103.
[http://dx.doi.org/10.1145/2001269.2001295]
[71]
Brosch T, Tam R. Manifold learning of brain MRIs by deep learning. Lect Notes Comput Sci 2013; 16(Pt 2): 633-40.
[http://dx.doi.org/10.1007/978-3-642-40763-5_78] [PMID: 24579194]
[72]
Brosch T, Yoo Y, Li DKB, Traboulsee A, Tam R. Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning. Lect Notes Comput Sci 2014; 17(Pt 2): 462-9.
[http://dx.doi.org/10.1007/978-3-319-10470-6_58] [PMID: 25485412]
[73]
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]
[74]
Pinaya WHL, Gadelha A, Doyle OM, et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci Rep 2016; 6: 38897.
[http://dx.doi.org/10.1038/srep38897] [PMID: 27941946]
[75]
Ortiz A, Munilla J, Górriz JM, Ramírez J. Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Int J Neural Syst 2016; 26(7): 1650025.
[http://dx.doi.org/10.1142/S0129065716500258] [PMID: 27478060]
[76]
Carneiro G, Nascimento JC, Freitas A. The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Trans Image Process 2012; 21(3): 968-82.
[http://dx.doi.org/10.1109/TIP.2011.2169273] [PMID: 21947526]
[77]
Carneiro G, Nascimento JC. Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Trans Pattern Anal Mach Intell 2013; 35(11): 2592-607.
[http://dx.doi.org/10.1109/TPAMI.2013.96] [PMID: 24051722]
[78]
Ngo TA, Lu Z, Carneiro G. 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: 159-71.
[http://dx.doi.org/10.1016/j.media.2016.05.009] [PMID: 27423113]
[79]
Azizi S, Imani F, Ghavidel S, et al. Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study. Int J CARS 2016; 11(6): 947-56.
[http://dx.doi.org/10.1007/s11548-016-1395-2] [PMID: 27059021]
[80]
Akhavan Aghdam M, Sharifi A, Pedram MM. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J Digit Imaging 2018; 31(6): 895-903.
[http://dx.doi.org/10.1007/s10278-018-0093-8] [PMID: 29736781]
[81]
Li H, Li X, Ramanathan M, Zhang A. Identifying informative risk factors and predicting bone disease progression via deep belief networks. Methods 2014; 69(3): 257-65.
[http://dx.doi.org/10.1016/j.ymeth.2014.06.011] [PMID: 24979059]
[82]
Reddy AVN, Krishna CP, Mallick PK, et al. Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks. J Big Data 2020; 7: 35.
[http://dx.doi.org/10.1186/s40537-020-00311-y]
[83]
Salakhutdinov R, Hinton G. Deep Boltzmann machines. Artificial Intelligence and Statistics PMLR 2009; 448-55.
[84]
Salakhutdinov R, Hinton G. An efficient learning procedure for deep Boltzmann machines. Neural Comput 2012; 24(8): 1967-2006.
[http://dx.doi.org/10.1162/NECO_a_00311] [PMID: 22509963]
[85]
Salakhutdinov R. Learning deep generative models. Annu Rev Stat Appl 2015; 2: 361-85.
[http://dx.doi.org/10.1146/annurev-statistics-010814-020120]
[86]
Goodfellow I, Mirza M, Courville A, Bengio Y. Multi-prediction deep Boltzmann machines. Adv Neural Inf Process Syst 2013; 26: 548-56.
[87]
Dinggang S, Wu G. SukHeung-Il. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 2017; 19: 221-48.
[88]
Suk H-I, Lee S-W, Shen D. Alzheimer’s Disease Neuroimaging Initiative. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 2014; 101: 569-82.
[http://dx.doi.org/10.1016/j.neuroimage.2014.06.077] [PMID: 25042445]
[89]
Cao Y, Steffey S, He J, et al. Medical image retrieval: A multimodal approach. Cancer Inform 2015; 13(Suppl. 3): 125-36.
[PMID: 26309389]
[90]
Wu J, Ruan S, Mazur TR, et al. Heart motion tracking on cine MRI based on a deep Boltzmann machine-driven level set method. Proc IEEE Int Symp Biomed Imaging 2018; 1153-6.
[http://dx.doi.org/10.1109/ISBI.2018.8363775]
[91]
Jeyaraj P, Nadar ERS. Deep Boltzmann machine algorithm for accurate medical image analysis for classification of cancerous region. Cognitive Computation Sys 2019; 1(3): 85-90.
[http://dx.doi.org/10.1049/ccs.2019.0004]
[92]
Goodfellow JP-A, Mirza M, Xu B, Warde-Farley D. Generative adversarial nets. Adv Neural Inf Process Syst 2014; 63: 2672-80.
[93]
Hu Y, Gibson E, Lee L-L, et al. Freehand ultrasound image simulation with spatially-conditioned generative adversarial networks. Lect Notes Comput Sci 2017; 10555: 105-15.
[http://dx.doi.org/10.1007/978-3-319-67564-0_11]
[94]
Bi L, Kim J, Kumar A, et al. Synthesis of positron emission tomography (PET) images via multi-channel generative adversarial networks (GANs). Lect Notes Comput Sci 2017; 10555: 43-51.
[http://dx.doi.org/10.1007/978-3-319-67564-0_5]
[95]
Bi L, Feng D, Kim J. Dual-path adversarial learning for fully convolutional network (FCN)-based medical image segmentation. Vis Comput 2018; 34(6-8): 1043-52.
[http://dx.doi.org/10.1007/s00371-018-1519-5]
[96]
Iqbal T, Ali H. Generative adversarial network for medical images (MI-GAN). J Med Syst 2018; 42(11): 231.
[http://dx.doi.org/10.1007/s10916-018-1072-9] [PMID: 30315368]
[97]
Canas K, Liu X, Ubiera B, et al. Scalable biomedical image synthesis with GAN. ACM International Conference Proceeding Series. Article No. 95: 1-3.
[http://dx.doi.org/10.1145/3219104.3229261]
[98]
Mardani M, Gong E, Cheng JY, et al. Deep generative adversarial neural networks for compressive sensing MRI. IEEE Trans Med Imaging 2019; 38(1): 167-79.
[http://dx.doi.org/10.1109/TMI.2018.2858752] [PMID: 30040634]
[99]
Wang Y, Yu B, Wang L, et al. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage 2018; 174: 550-62.
[http://dx.doi.org/10.1016/j.neuroimage.2018.03.045] [PMID: 29571715]
[100]
Liu Z, Bicer T, Kettimuthu R, Gursoy D, De Carlo F, Foster I. TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion. J Opt Soc Am A Opt Image Sci Vis 2020; 37(3): 422-34.
[http://dx.doi.org/10.1364/JOSAA.375595] [PMID: 32118926]
[101]
Kang E, Koo HJ, Yang DH, Seo JB, Ye JC. Cycle-consistent adversarial denoising network for multiphase coronary CT angiography. Med Phys 2019; 46(2): 550-62.
[http://dx.doi.org/10.1002/mp.13284] [PMID: 30449055]
[102]
Frid-Adar M, Diamant I, Klang E, et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 2018; 321(10): 321-31.
[http://dx.doi.org/10.1016/j.neucom.2018.09.013]
[103]
Chuquicusma MJM, Hussein S, Burt J, et al. How to fool radiologists with generative adversarial networks?A visual turing test for lung cancer diagnosis. Proc IEEE Int Symp Biomed Imaging 2018; 2018: 240-4.
[http://dx.doi.org/10.1109/ISBI.2018.8363564]
[104]
Mondal AK, Dolz J, Desrosiers C. Few-shot 3D multi-modal medical image segmentation using generative adversarial learning. arXiv preprint 2018.
[105]
Salehinejad H, Valaee S, Dowdell T, et al. Generalization of deep neural networks for chest pathology classification in X-rays using generative adversarial networks. Proceeding IEEE International Conference on Acoustics, Speech and Signal Processing. 990-4.
[http://dx.doi.org/10.1109/ICASSP.2018.8461430]
[106]
Madani A, Moradi M, Karargyris A, et al. Semi-Supervised Learning with Generative Adversarial Networks for Chest X-Ray Classification with Ability of Data Domain Adaptation. 2018 15th IEEE Int Symp Biomed Imaging (ISBI 2018) Washington, DC 2018; 1038-42.
[107]
Baur C, Albarqouni S, Navab N. MelanoGANs : High resolution skin lesion synthesis with GANs. arXiv preprint 2018.
[108]
Lahiri A, Jain V, Mondal A, et al. Retinal vessel segmentation under extreme low annotation: A gan based semi-supervised approach. IEEE International Conference on Image Processing (ICIP). 418-22.
[http://dx.doi.org/10.1109/ICIP40778.2020.9190882]
[109]
Costa P, Galdran A, Meyer MI, et al. End-to-End Adversarial Retinal Image Synthesis. IEEE Trans Med Imaging 2018; 37(3): 781-91.
[http://dx.doi.org/10.1109/TMI.2017.2759102] [PMID: 28981409]
[110]
Zhao H, Li H, Maurer-Stroh S, Cheng L. Synthesizing retinal and neuronal images with generative adversarial nets. Med Image Anal 2018; 49: 14-26.
[http://dx.doi.org/10.1016/j.media.2018.07.001] [PMID: 30007254]
[111]
Shin HC, Tenenholtz NA, Rogers JK, et al. Medical image synthesis for data augmentation and anonymization using generative adversarial networks. Lect Notes Comput Sci 2018; 11037: 1-11.
[http://dx.doi.org/10.1007/978-3-030-00536-8_1]
[112]
Mok TCW, Chung ACS. Learning data augmentation for brain tumor segmentation with coarse-to-fine generative adversarial networks. Lect Notes Comput Sci 2019; 11383.
[http://dx.doi.org/10.1007/978-3-030-11723-8_7]
[113]
Tom F, Sheet D. Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning. Proceedings IEEE Int Symp Biomed Imaging. Washington DC. 2018; pp. 1174-7.
[http://dx.doi.org/10.1109/ISBI.2018.8363780]
[114]
Jiang Y, Chen H, Loew M, Ko H. COVID-19 CT image synthesis with a conditional generative adversarial network. IEEE J Biomed Health Inform 2021; 25(2): 441-52.
[http://dx.doi.org/10.1109/JBHI.2020.3042523] [PMID: 33275588]
[115]
Zhang Y, Miao S, Mansi T, Liao R. Unsupervised X-ray image segmentation with task driven generative adversarial networks. Med Image Anal 2020; 62: 101664.
[http://dx.doi.org/10.1016/j.media.2020.101664] [PMID: 32120268]
[116]
Rezaei M, Yang H, Meinel C. Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation. Multimedia Tools Appl 2020; 79(21): 15329-48.
[http://dx.doi.org/10.1007/s11042-019-7305-1]
[117]
Lei B, Xia Z, Jiang F, et al. Skin lesion segmentation via generative adversarial networks with dual discriminators. Med Image Anal 2020; 64: 101716.
[http://dx.doi.org/10.1016/j.media.2020.101716] [PMID: 32492581]
[118]
Singh NK, Raza K. Medical image generation using generative adversarial networks. Stud Comput Intell 2021; 932: 77-96.
[119]
Gopal A, Gandhimaruthian L, Ali J. Role of General Adversarial Networks in Mammogram Analysis: A Review. Curr Med Imaging Rev 2020; 16(7): 863-77.
[http://dx.doi.org/10.2174/1573405614666191115102318] [PMID: 33059556]
[120]
Wolterink JM, Kamnitsas K, Ledig C, Išgum I. Generative adversarial networks and adversarial methods in biomedical image analysis. arXiv preprint 2018.
[121]
Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review. Med Image Anal 2019; 58: 101552.
[http://dx.doi.org/10.1016/j.media.2019.101552] [PMID: 31521965]
[122]
Kazeminia S, Baur C, Kuijper A, et al. GANs for medical image analysis. Artif Intell Med 2020; 109: 101938.
[http://dx.doi.org/10.1016/j.artmed.2020.101938]
[123]
Golea NE-H, Melkemi KE. ROI-based fragile watermarking for medical image tamper detection. Int J High Perform Comput Network 2019; 13(2): 199-210.
[http://dx.doi.org/10.1504/IJHPCN.2019.097508]
[124]
Dorgham O, Al-Rahamneh B, Ai-Hadidi M, Khatatneh KF, Almomani A. Enhancing the security of exchanging and storing DICOM medical images on the cloud. Int J Cloud Appl Comput 2018; 8(1): 154-72.
[http://dx.doi.org/10.4018/IJCAC.2018010108]
[125]
Guo P, Evans A, Bhattacharya P. Nuclei segmentation for quantification of brain tumors in digital pathology images. Int J Softw Sci Comput Intell 2018; 10(2): 36-49.
[http://dx.doi.org/10.4018/IJSSCI.2018040103]
[126]
Liu H, Guo Q, Wang G, Gupta BB, Zhang C. Medical image resolution enhancement for healthcare using nonlocal self-similarity and low-rank prior. Multimedia Tools Appl 2019; 78(7): 9033-50.
[http://dx.doi.org/10.1007/s11042-017-5277-6]
[127]
Ghoneim A, Muhammad G, Amin SU, et al. Medical Image Forgery Detection for Smart Healthcare. IEEE Commun Mag 2018; 56(4): 33-7.
[http://dx.doi.org/10.1109/MCOM.2018.1700817]
[128]
Zhu Q, Du B, Yan P. Boundary-weighted domain adaptive neural network for prostate MR image segmentation. IEEE Trans Med Imaging 2020; 39(3): 753-63.
[http://dx.doi.org/10.1109/TMI.2019.2935018] [PMID: 31425022]
[129]
Zhu Q, Bo D, Turkbey B, et al. Exploiting interslice correlation for MRI prostate image segmentation, from recursive neural networks aspect. Complexity 2018; 2018: 4185279.
[http://dx.doi.org/10.1155/2018/4185279]

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