Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.
References
[1]
Pandey, B., Pandey, D.K., Mishra, B.P. and Rhmann, W. (2021) A Comprehensive Survey of Deep Learning in the Field of Medical Imaging and Medical Natural Language Processing: Challenges and Research Directions. Journal of King Saud University—Computer and Information Sciences, in press. https://doi.org/10.1016/j.jksuci.2021.01.007
[2]
Nogales, A., García-Tejedor, á.J., Monge, D., Vara, J.S. and Antón, C. (2021) A Survey of Deep Learning Models in Medical Therapeutic Areas. Artificial Intelligence in Medicine, 112, Article ID: 102020. https://doi.org/10.1016/j.artmed.2021.102020
[3]
Xie, X.Z., Niu, J.W., Liu, X.F., Chen, Z.S., Tang, S.J. and Yu, S. (2021) A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis. Medical Image Analysis, 69, Article ID: 101985. https://doi.org/10.1016/j.media.2021.101985
[4]
Piccialli, F., Di Somma, V., Giampaolo, F., Cuomo, S. and Fortino, G. (2021) A Survey on Deep Learning in Medicine: Why, How and When? Information Fusion, 66, 111-137. https://doi.org/10.1016/j.inffus.2020.09.006
[5]
Shin, H.-C., Lu, L. and Summers, R.M. (2017) Chapter 17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning. In: Zhou, S.K., Greenspan, H. and Shen, D.G., Eds., Deep Learning for Medical Image Analysis, Academic Press, Cambridge, MA, 405-421. https://doi.org/10.1016/B978-0-12-810408-8.00023-7
[6]
Wang, X., Yang, X., Dou, H.R., Li, S.L., Heng, P. and Ni, D. (2019) Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, 19-22 May 2019, 1-5. https://doi.org/10.1109/BHI.2019.8834615
[7]
Sharma, S. and Mehra, R. (2020) Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—A Comparative Insight. Journal of Digital Imaging, 33, 632-654. https://doi.org/10.1007/s10278-019-00307-y
[8]
Pattanaik, P.A., Mittal, M., Khan, M.Z. and Panda, S.N. (2020) Malaria Detection Using Deep Residual Networks with Mobile Microscopy. Journal of King Saud University—Computer and Information Sciences, in Press. https://doi.org/10.1016/j.jksuci.2020.07.003
[9]
He, Y.T., Yang, G.Y., Chen, Y., Kong, Y.Y., Wu, J.S., et al. (2019) DPA-DenseBiasNet: Semi-Supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy. In: Shen, D., et al., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, Vol. 11769, Springer, Cham, 139-147. https://doi.org/10.1007/978-3-030-32226-7_16
[10]
Zhu, Y., Wang, M.D., Tong, L. and Deshpande, S.R. (2019) Improved Prediction on Heart Transplant Rejection Using Convolutional Autoencoder and Multiple Instance Learning on Whole-Slide Imaging. IEEE EMBS International Conference on Biomedical and Health Informatics, Chicago, IL, 19-22 May 2019, 1-4. https://doi.org/10.1109/BHI.2019.8834632
[11]
Zhang, Z., Liang, X., Dong, X., Xie, Y. and Cao, G. (2018) A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution. IEEE Transactions on Medical Imaging, 37, 1407-1417. https://doi.org/10.1109/TMI.2018.2823338
[12]
Shin, H.-C., Lu, L., Kim, L., Seff, A., Yao, J.H. and Summers, R.M. (2016) Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation. Journal of Machine Learning Research, 17, 1-31.
[13]
Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B. and Yang, G.Z. (2017) Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21, 4-21. https://doi.org/10.1109/JBHI.2016.2636665
[14]
Vizcarra, J., Place, R., Tong, L., Gutman, D. and Wang, M.D. (2019) Fusion in Breast Cancer Histology Classification. Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Niagara Falls, NY, 7-10 September 2019, 485-493. https://doi.org/10.1145/3307339.3342166
[15]
Zhao, Y., Dong, Q., Zhang, S., Zhang, W., Chen, H., Jiang, X., Guo, L., Hu, X., Han, J. and Liu, T. (2018) Automatic Recognition of fMRI-Derived Functional Networks Using 3-D Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, 65, pp1975-1984. https://doi.org/10.1109/TBME.2017.2715281
[16]
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M. and Thrun, S. (2017) Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature. Nature, 542, 115-118. https://doi.org/10.1038/nature21056
[17]
Zhou, H., Sun, J., Yacoob, Y. and Jacobs, D.W. (2018) Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Faces. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 6238-6247. https://doi.org/10.1109/CVPR.2018.00653
[18]
Zhu, W.T., Liu, C.C., Fan, W. and Xie, X. (2018) DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, 12-15 March 2018, 673-681. https://doi.org/10.1109/WACV.2018.00079
[19]
Zhang, R., Zheng, Y., Poon, C.C.Y., Shen, D. and Lau, J.Y.W. (2018) Polyp Detection during Colonoscopy Using a Regression-Based Convolutional Neural Network with a Tracker. Pattern Recognition, 83, 209-219. https://doi.org/10.1016/j.patcog.2018.05.026
[20]
Cheimariotis, G.A., Riga, M., Toutouzas, K., Tousoulis, D., Katsaggelos, A. and Maglaveras, N. (2019) Deep Learning Method to Detect Plaques in IVOCT Images. In: Lin, K.-P., Magjarevic, R. and de Carvalho, P., Eds., Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices. ICBHI 2019. IFMBE Proceedings, Vol. 74, Springer, Cham, 389-395. https://doi.org/10.1007/978-3-030-30636-6_53
[21]
Halevy, A., Norvig, P. and Pereira, F. (2009) The Unreasonable Effectiveness of Data. IEEE Intelligent Systems, 24, 8-12. https://doi.org/10.1109/MIS.2009.36
[22]
Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., et al. (2016) Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging, 35, 1285-1298. https://doi.org/10.1109/TMI.2016.2528162
[23]
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B. and Liang, J.M. (2016) Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning. IEEE Transactions on Medical Imaging, 35, 1299-1312. https://doi.org/10.1109/TMI.2016.2535302
[24]
Yap, M.H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., Davison, A.K., Marti, R., Yap, M.H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., Davison, A.K. and Marti, R. (2018) Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks. IEEE Journal of Biomedical and Health Informatics, 22, 1218-1226. https://doi.org/10.1109/JBHI.2017.2731873
[25]
Näppi, J.J., Hironaka, T., Regge, D. and Yoshida, H. (2016) Deep Transfer Learning of Virtual Endoluminal Views for the Detection of Polyps in CT Colonography. Medical Imaging 2016: Computer-Aided Diagnosis, 9785, 97852B. https://doi.org/10.1117/12.2217260
[26]
Zhang, R., Zheng, Y., Mak, T.W., Yu, R., Wong, S.H., Lau, J.Y. and Poon, C.C. (2017) Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain. IEEE Journal of Biomedical and Health Informatics, 21, 41-47. https://doi.org/10.1109/JBHI.2016.2635662
[27]
Tran, D., Bourdev, L., Fergus, R., Torresani, L. and Paluri, M. (2015) Learning Spatiotemporal Features with 3D Convolutional Networks. IEEE International Conference on Computer Vision (ICCV), Santiago, 7-13 December 2015, 4489-4497. https://doi.org/10.1109/ICCV.2015.510
[28]
Samala, R.K., Chan, H.P., Hadjiiski, L.M., Helvie, M.A., Cha, K.H. and Richter, C.D. (2017) Multi-Task Transfer Learning Deep Convolutional Neural Network: Application to Computer-Aided Diagnosis of Breast Cancer on Mammograms. Physics in Medicine & Biology, 62, 8894-8908. https://doi.org/10.1088/1361-6560/aa93d4
[29]
Samala, R.K., Chan, H.-P., Hadjiiski, L., Helvie, M.A., Richter, C.D. and Cha, K.H. (2019) Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets. IEEE Transactions on Medical Imaging, 38, 686-696. https://doi.org/10.1109/TMI.2018.2870343
[30]
Liao, Q., Ding, Y., Jiang, Z.L., Wang, X., Zhang, C.K. and Zhang, Q. (2019) Multi-Task Deep Convolutional Neural Network for Cancer Diagnosis. Neurocomputing, 348, 66-73. https://doi.org/10.1016/j.neucom.2018.06.084
[31]
Ben-Cohen, A., Klang, E., Raskin, S.P., Soffer, S., Ben-Haim, S., Konen, E., Amitai, M.M. and Greenspan, H. (2019) Cross-Modality Synthesis from CT to PET Using FCN and GAN Networks for Improved Automated Lesion Detection. Engineering Applications of Artificial Intelligence, 78, 186-194. https://doi.org/10.1016/j.engappai.2018.11.013
[32]
Zhao, J., Li, D., Kassam, Z., Howey, J., Chong, J., Chen, B. and Li, S. (2020) Tripartite-GAN: Synthesizing Liver Contrast-Enhanced MRI to Improve Tumor Detection. Medical Image Analysis, 63, Article ID: 101667. https://doi.org/10.1016/j.media.2020.101667
[33]
Zhang, J., Saha, A., Zhu, Z. and Mazurowski, M.A. (2019) Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI with Application to Radiogenomics. IEEE Transactions on Medical Imaging, 38, 435-447. https://doi.org/10.1109/TMI.2018.2865671
[34]
Yu, F., Zhao, J., Gong, Y., Wang, Z., Li, Y., Yang, F. and Zhang, L. (2019) Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images. In: Shen, D., et al., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, Vol. 11765, Springer, Cham, 714-722. https://doi.org/10.1007/978-3-030-32245-8_79
[35]
Chen, C., Biffi, C., Tarroni, G., Petersen, S., Bai, W. and Rueckert, D. (2019) Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-View Images. In: Shen, D., et al., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, Vol. 11765, Springer, Cham, 523-531. https://doi.org/10.1007/978-3-030-32245-8_58
[36]
Valindria, V.V., et al. (2018) Multi-Modal Learning from Unpaired Images: Application to Multi-Organ Segmentation in CT and MRI. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, 12-15 March 2018, 547-556. https://doi.org/10.1109/WACV.2018.00066
[37]
Qin, C., Schlemper, J., Caballero, J., Price, A.N., Hajnal, J.V. and Rueckert, D. (2019) Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction. IEEE Transactions on Medical Imaging, 38, 280-290. https://doi.org/10.1109/TMI.2018.2863670
[38]
Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N. and Rueckert, D. (2018) A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. IEEE Transactions on Medical Imaging, 37, 491-503. https://doi.org/10.1109/TMI.2017.2760978
[39]
Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P.L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y., Firmin, D., Keegan, J., Slabaugh, G., Arridge, S., Ye, X., Guo, Y., Yu, S., Liu, F., Firmin, D., Dragotti, P.L., Yang, G. and Dong, H. (2018) DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. IEEE Transactions on Medical Imaging, 37, 1310-1321. https://doi.org/10.1109/TMI.2017.2785879
[40]
Ben Yedder, H., Shokoufi, M., Cardoen, B., Golnaraghi, F. and Hamarneh, G. (2019) Limited-Angle Diffuse Optical Tomography Image Reconstruction Using Deep Learning. In: Shen, D., et al., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, Vol. 11764, Springer, Cham, 66-74. https://doi.org/10.1007/978-3-030-32239-7_8
[41]
Ahmad, J., Sajjad, M., Mehmood, I. and Baik, S.W. (2017) SiNC: Saliency-Injected Neural Codes for Representation and Efficient Retrieval of Medical Radiographs. PLoS ONE, 12, e0181707. https://doi.org/10.1371/journal.pone.0181707
[42]
Khatami, A., Babaie, M., Tizhoosh, H.R., Khosravi, A., Nguyen, T. and Nahavandi, S. (2018) A Sequential Search-Space Shrinking Using CNN Transfer Learning and a Radon Projection Pool for Medical Image Retrieval. Expert Systems with Applications, 100, 224-233. https://doi.org/10.1016/j.eswa.2018.01.056
[43]
Swati, Z.N.K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S. and Lu, J. (2019) Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning. IEEE Access, 7, 17809-17822. https://doi.org/10.1109/ACCESS.2019.2892455
[44]
Pham, H.H., Le, T.T., Tran, D.Q., Ngo, D.T. and Nguyen, H.Q. (2019) Interpreting Chest X-Rays via CNNs That Exploit Hierarchical Disease Dependencies and Uncertainty Labels. arXiv: 1911.06475 https://doi.org/10.1101/19013342
[45]
Bekker, A.J. and Goldberger, J. (2016) Training Deep Neural-Networks Based on Unreliable Labels. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 20-25 March 2016, 2682-2686. https://doi.org/10.1109/ICASSP.2016.7472164
[46]
Matuszewski, D.J. and Sintorn, I.M. (2018) Minimal Annotation Training for Segmentation of Microscopy Images. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington DC, 4-7 April 2018, 387-390. https://doi.org/10.1109/ISBI.2018.8363599
[47]
Ren, M., Zeng, W., Yang, B. and Urtasun, R. (2018) Learning to Reweight Examples for Robust Deep Learning. Proceedings of the 35th International Conference on Machine Learning, PMLR, 80, 4334-4343.
[48]
Xue, C., Dou, Q., Shi, X., Chen, H. and Heng, P.A. (2019) Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, 8-11 April 2019, 1280-1283. https://doi.org/10.1109/ISBI.2019.8759203
[49]
Mirikharaji, Z., Yan, Y. and Hamarneh, G. (2019) Learning to Segment Skin Lesions from Noisy Annotations. In: Wang, Q., et al., Eds., Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART 2019, MIL3ID 2019. Lecture Notes in Computer Science, Vol. 11795, Springer, Cham, 207-215. https://doi.org/10.1007/978-3-030-33391-1_24
[50]
Nie, D., Gao, Y., Wang, L. and Shen, D. (2018) ASDNet: Attention Based Semi-Supervised Deep Networks for Medical Image Segmentation. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C. and Fichtinger, G., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, Vol. 11073, Springer, Cham, 370-378. https://doi.org/10.1007/978-3-030-00937-3_43
[51]
Fries, J.A., Varma, P., Chen, V.S., Xiao, K., Tejeda, H., Saha, P., Dunnmon, J., Chubb, H., Maskatia, S., Fiterau, M., Delp, S., Ashley, E., Ré, C. and Priest, J.R. (2019) Weakly Supervised Classification of Aortic Valve Malformations Using Unlabeled Cardiac MRI Sequences. Nature Communications, 10, Article No. 3111. https://doi.org/10.1038/s41467-019-11012-3
[52]
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H. and Wang, Y. (2017) Artificial Intelligence in Healthcare: Past, Present and Future. Stroke and Vascular Neurology, 2, 230-243. https://doi.org/10.1136/svn-2017-000101
[53]
Miller, D.D. and Brown, E.W. (2018) Artificial Intelligence in Medical Practice: The Question to the Answer. The American Journal of Medicine, 131, 129-133. https://doi.org/10.1016/j.amjmed.2017.10.035
[54]
Jang, H.J. and Cho, K.O. (2019) Applications of Deep Learning for the Analysis of Medical Data. Archives of Pharmacal Research, 42, 492-504. https://doi.org/10.1007/s12272-019-01162-9
[55]
Bakator, M. and Radosav, D. (2018) Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction, 2, 47. https://doi.org/10.3390/mti2030047
[56]
Lundervold, A.S. and Lundervold, A. (2019) An Overview of Deep Learning in Medical Imaging Focusing on MRI. Zeitschrift für Medizinische Physik, 29, 102-127. https://doi.org/10.1016/j.zemedi.2018.11.002
[57]
Hecht-Nielsen, R. (1988) Neurocomputing: Picking the Human Brain. IEEE Spectrum, 25, 36-41. https://doi.org/10.1109/6.4520
[58]
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) ImageNet Classification with Deep Convolutional Neural Networks. In: Pereira, F., Burges, C.J.C., Bottou, L. and Weinberger, K.Q., Eds., Advances in Neural Information Processing Systems, Vol. 25, Curran Associates, Inc., Red Hook, NY, 1097-1105.
[59]
Arasu, A. and Garcia-Molina, H. (2003) Extracting Structured Data from Web Pages. Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, CA, 9-12 June 2003, 337-348. https://doi.org/10.1145/872757.872799
[60]
Velicer, W.F. and Molenaar, P.C. (2012) Time Series Analysis for Psychological Research. In: Weiner, I., Schinka, J.A. and Velicer, W.F., Eds., Handbook of Psychology, 2nd Edition, John Wiley & Sons, Inc, Hoboken. https://doi.org/10.1002/9781118133880.hop202022
[61]
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W. and Jackel, L.D. (1989) Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1, 541-551. https://doi.org/10.1162/neco.1989.1.4.541
[62]
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90. https://doi.org/10.1145/3065386
[63]
Kipf, T.N. and Welling, M. (2016) Semi-Supervised Classification with Graph Convolutional Networks. 5th International Conference on Learning Representations (ICLR-17). arXiv: 1609.02907.
Simonyan, K. and Zisserman, A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv: 1409.1556.
[66]
León, J., Escobar, J.J., Ortiz, A., Ortega, J., González, J., Martín-Smith, P., Gan, J.Q. and Damas, M. (2020) Deep Learning for EEG-Based Motor Imagery Classification: Accuracy-Cost Trade-Off. PLoS ONE, 15, e0234178. https://doi.org/10.1371/journal.pone.0234178
[67]
Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
[68]
Sathya, R. and Abraham, A. (2013) Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification. International Journal of Advanced Research in Artificial Intelligence (IJARAI), 2, 34-38. https://doi.org/10.14569/IJARAI.2013.020206
[69]
Gondara, L. (2016) Medical Image Denoising Using Convolutional Denoising Autoencoders. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, 12-15 December 2016, 241-246. https://doi.org/10.1109/ICDMW.2016.0041
[70]
Zhou, B.L., Khosla, A., Lapedriza, A., Torralba, A. and Oliva, A. (2016) Places: An Image Database for Deep Scene Understanding. arXiv: 1610.02055
[71]
Nowling, R.J., et al. (2019) Classification before Segmentation: Improved U-Net Prostate Segmentation. 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, 19-22 May 2019, 1-4. https://doi.org/10.1109/BHI.2019.8834494
[72]
Pesteie, M, Abolmaesumi, P. and Rohling, R.N. (2019) Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders. IEEE Transactions on Medical Imaging, 38, 2807-2820. https://doi.org/10.1109/TMI.2019.2914656
[73]
Yu, E.M., Iglesias, J.E., Dalca, A.V. and Sabuncu, M.R. (2020) An Auto-Encoder Strategy for Adaptive Image Segmentation. Proceedings of the Third Conference on Medical Imaging with Deep Learning, PMLR, 121, 881-891.
[74]
Uzunova, H., Schultz, S., Handels, H., et al. (2019) Unsupervised Pathology Detection in Medical Images Using Conditional Variational Autoencoders. International Journal of Computer Assisted Radiology and Surgery 14, 451-461. https://doi.org/10.1007/s11548-018-1898-0
[75]
Chen, M., Shi, X., Zhang, Y., Wu, D. and Guizani, M. (2017) Deep Features Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network. IEEE Transactions on Big Data.
[76]
Saltz, J., et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports, 23, 181-193.e7.
[77]
Apostolopoulos, S., Ciller, C., De Zanet, S., Wolf, S. and Sznitman, R. (2017) RetiNet: Automatic AMD Identification in OCT Volumetric Data. Investigative Ophthalmology & Visual Science, 58, 387.
[78]
Lam, C., Yu, C., Huang, L. and Rubin, D. (2018) Retinal Lesion Detection with Deep Learning Using Image Patches. Investigative Ophthalmology & Visual Science, 59, 590-596. https://doi.org/10.1167/iovs.17-22721
[79]
Choi, H., Ha, S., Im, H.J., Paek, S.H. and Lee, D.S. (2017) Refining Diagnosis of Parkinson’s Disease with Deep Learning-Based Interpretation of Dopamine Transporter Imaging. NeuroImage: Clinical, 16, 586-594. https://doi.org/10.1016/j.nicl.2017.09.010
[80]
Rajaraman, S., Antani, S.K., Poostchi, M., Silamut, K., Hossain, M.A., Maude, R.J., Jaeger, S. and Thoma, G.R. (2018) Pre-Trained Convolutional Neural Networks as Feature Extractors toward Improved Malaria Parasite Detection in Thin Blood Smear Images. PeerJ, 6, e4568. https://doi.org/10.7717/peerj.4568
[81]
Nielsen, A., Hansen, M.B., Tietze, A. and Mouridsen, K. (2018) Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning. Stroke, 49, 1394-1401. https://doi.org/10.1161/STROKEAHA.117.019740
[82]
Lee, H.C., Ryu, H.G., Chung, E.J. and Jung, C.W. (2018) Prediction of Bispectral Index during Target-Controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach. Anesthesiology, 128, 492-501. https://doi.org/10.1097/ALN.0000000000001892
[83]
Zeng, L.L., Wang, H., Hu, P., Yang, B., Pu, W., Shen, H., Chen, X., Liu, Z., Yin, H., Tan, Q., Wang, K. and Hu, D. (2018) Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI. EBioMedicine, 30, 74-85. https://doi.org/10.1016/j.ebiom.2018.03.017
[84]
Ghesu, F.C., Georgescu, B., Zheng, Y., Hornegger, J. and Comaniciu, D. (2015) Marginal Space Deep Learning: Efficient Architecture for Detection in Volumetric Image Data. In: Navab, N., Hornegger, J., Wells, W. and Frangi, A., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, Vol. 9349, Springer, Cham, 710-718. https://doi.org/10.1007/978-3-319-24553-9_87
[85]
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A. and Mougiakakou, S. (2016) Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Transactions on Medical Imaging, 35, 1207-1216. https://doi.org/10.1109/TMI.2016.2535865
[86]
Yasaka, K., Akai, H., Kunimatsu, A., Abe, O. and Kiryu, S. (2018) Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MR Images. Radiology, 287, 146-155. https://doi.org/10.1148/radiol.2017171928
[87]
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. and Bengio, Y. (2014) Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, October 2014, 1724-1734. https://doi.org/10.3115/v1/D14-1179
[88]
Leibig, C., Allken, V., Ayhan, M.S., Berens, P. and Wahl, S. (2017) Leveraging Uncertainty Information from Deep Neural Networks for Disease Detection. Scientific Reports, 7, Article No. 17816. https://doi.org/10.1038/s41598-017-17876-z
[89]
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C.I., Mann, R., den Heeten, A. and Karssemeijer, N. (2017) Large Scale Deep Learning for Computer Aided Detection of Mammographic Lesions. Medical Image Analysis, 35, 303-312. https://doi.org/10.1016/j.media.2016.07.007
[90]
Schuster, M. and Paliwal, K.K. (1997) Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45, 2673-2681. https://doi.org/10.1109/78.650093
[91]
Kim, E.K., Kim, H.E., Han, K., Kang, B.J., Sohn, Y.M., Woo, O.H. and Lee, C.W. (2018) Applying Data-Driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study. Scientific Reports, 8, Article No. 2762. https://doi.org/10.1038/s41598-018-21215-1
[92]
Heinsfeld, A.S., Franco, A.R., Craddock, R.C., Buchweitz, A. and Meneguzzi, F. (2018) Identification of Autism Spectrum Disorder Using Deep Learning and the ABIDE Dataset. NeuroImage: Clinical, 17, 16-23. https://doi.org/10.1016/j.nicl.2017.08.017
[93]
Suk, H.I., Lee, S.W. and Shen, D. (2014) Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis. NeuroImage, 101, 569-582. https://doi.org/10.1016/j.neuroimage.2014.06.077
[94]
Hinton, G., Vinyals, O. and Dean, J. (2015) Distilling the Knowledge in a Neural Network. arXiv: 1503.02531
[95]
van Grinsven, M.J., van Ginneken, B., Hoyng, C.B., Theelen, T. and Sanchez, C.I. (2016) Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images. IEEE Transactions on Medical Imaging, 35, 1273-1284. https://doi.org/10.1109/TMI.2016.2526689
[96]
Wang, J., Yang, X., Cai, H., et al. (2016) Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning. Scientific Reports, 6, Article No. 27327. https://doi.org/10.1038/srep27327
[97]
Kooi, T., van Ginneken, B., Karssemeijer, N. and den Heeten, A. (2017) Discriminating Solitary Cysts from Soft Tissue Lesions in Mammography Using a Pretrained Deep Convolutional Neural Network. Medical Physics, 44, 1017-1027. https://doi.org/10.1002/mp.12110
[98]
Fu, H., Cheng, J., Xu, Y., Zhang, C., Wong, D.W.K., Liu, J. and Cao, X. (2018) Disc-Aware Ensemble Network for Glaucoma Screening from Fundus Image. IEEE Transactions on Medical Imaging, 37, 2493-2501. https://doi.org/10.1109/TMI.2018.2837012
[99]
Yu, C., Yang, S., Kim, W., Jung, J., Chung, K.Y., Lee, S.W. and Oh, B. (2018) Acral Melanoma Detection Using a Convolutional Neural Network for Dermoscopy Images. PLoS ONE, 13, e0193321. https://doi.org/10.1371/journal.pone.0193321
[100]
Topol, E. (2019) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books, Hachette, UK.
[101]
Wang, J., Ding, H., Bidgoli, F.A., Zhou, B., Iribarren, C., Molloi, S. and Baldi, P. (2017) Detecting Cardiovascular Disease from Mammograms with Deep Learning. IEEE Transactions on Medical Imaging, 36, 1172-1181. https://doi.org/10.1109/TMI.2017.2655486
[102]
Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1985) Learning Internal Representations by Error Propagation. California Univ San Diego La Jolla Inst for Cognitive Science.
[103]
Iakovidis, D.K., Georgakopoulos, S.V., Vasilakakis, M., Koulaouzidis, A. and Plagianakos, V.P. (2018) Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification. IEEE Transactions on Medical Imaging, 37, 2196-2210. https://doi.org/10.1109/TMI.2018.2837002
[104]
van der Burgh, H.K., Schmidt, R., Westeneng, H.J., de Reus, M.A., van den Berg, L.H. and van den Heuvel, M.P. (2016) Deep Learning Predictions of Survival Based on MRI in Amyotrophic Lateral Sclerosis. NeuroImage: Clinical, 13, 361-369. https://doi.org/10.1016/j.nicl.2016.10.008
[105]
Lee, C.S., Baughman, D.M. and Lee, A.Y. (2017) Deep Learning Is Effective for the Classification of OCT Images of Normal Versus Age-Related Macular Degeneration. Ophthalmology Retina, 1, 322-327. https://doi.org/10.1016/j.oret.2016.12.009
[106]
Hsieh, Y.J., Tseng, H.C., Chin, C.L., Shao, Y.H. and Tsai, T.Y. (2020) Based on DICOM RT Structure and Multiple Loss Function Deep Learning Algorithm in Organ Segmentation of Head and Neck Image. In: Lin, K.P., Magjarevic, R. and de Carvalho, P., Eds., Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices. ICBHI 2019. IFMBE Proceedings, Vol. 74, Springer, Cham, 428-435. https://doi.org/10.1007/978-3-030-30636-6_58
[107]
Ngo, T.A., Lu, Z. and Carneiro, G. (2017) Combining Deep Learning and Level Set for the Automated Segmentation of the Left Ventricle of the Heart from Cardiac Cine Magnetic Resonance. Medical Image Analysis, 35, 159-171. https://doi.org/10.1016/j.media.2016.05.009
[108]
Zhang, J., Xia, Y., Wu, Q. and Xie, Y.T. (2017) Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning. arXiv: 1706.09092
[109]
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A. and Campilho, A. (2017) Classification of Breast Cancer Histology Images Using Convolutional Neural Networks. PLoS ONE, 12, e0177544. https://doi.org/10.1371/journal.pone.0177544
[110]
Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K. and Li, S. (2017) Breast Cancer Multi-Classification from Histopathological Images with Structured Deep Learning Model. Scientific Reports, 7, Article No. 4172. https://doi.org/10.1038/s41598-017-04075-z
[111]
Zhang, X., Yu, F.X., Chang, S.-F. and Wang, S.J. (2015) Deep Transfer Network: Unsupervised Domain Adaptation. arXiv: 1503.00591
[112]
Hutchinson, B., Deng, L. and Yu, D. (2013) Tensor Deep Stacking Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1944-1957. https://doi.org/10.1109/TPAMI.2012.268
[113]
Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A. and Bengio, Y. (2018) Learning Deep Representations by Mutual Information Estimation and Maximization. arXiv: 1808.06670
[114]
Alom, M.Z., Yakopcic, C., Nasrin, M.S., Taha, T.M. and Asari, V.K. (2019) Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network. Journal of Digital Imaging, 32, 605-617. https://doi.org/10.1007/s10278-019-00182-7
[115]
Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P. and Saarakkala, S. (2018) Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach. Scientific Reports, 8, Article No. 1727. https://doi.org/10.1038/s41598-018-20132-7
[116]
Lee, J. and Nishikawa, R.M. (2018) Automated Mammographic Breast Density Estimation Using a Fully Convolutional Network. Medical Physics, 45, 1178-1190. https://doi.org/10.1002/mp.12763
[117]
Esses, S.J., Lu, X., Zhao, T., Shanbhogue, K., Dane, B., Bruno, M. and Chandarana, H. (2018) Automated Image Quality Evaluation of T2-Weighted Liver MRI Utilizing Deep Learning Architecture. Journal of Magnetic Resonance Imaging, 47, 723-728. https://doi.org/10.1002/jmri.25779
[118]
Serj, M.F., Lavi, B., Hoff, G. and Valls, D.P. (2018) A Deep Convolutional Neural Network for Lung Cancer Diagnostic. arXiv: 1804.08170
[119]
Du, X., et al. (2018) Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks. IEEE Transactions on Medical Imaging, 37, 1276-1287. https://doi.org/10.1109/TMI.2017.2787672
[120]
Han, S.S., Park, G.H., Lim, W., Kim, M.S., Na, J.I., Park, I. and Chang, S.E. (2018) Deep Neural Networks Show an Equivalent and Often Superior Performance to Dermatologists in Onychomycosis Diagnosis: Automatic Construction of Onychomycosis Datasets by Region-Based Convolutional Deep Neural Network. PLoS ONE, 13, e0191493. https://doi.org/10.1371/journal.pone.0191493
[121]
Kim, K.H., Choi, S.H. and Park, S.H. (2018) Improving Arterial Spin Labeling by Using Deep Learning. Radiology, 287, 658-666. https://doi.org/10.1148/radiol.2017171154
[122]
Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B. and Wang, T. (2015) Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning. IEEE Transactions on Biomedical Engineering, 62, 2421-2433. https://doi.org/10.1109/TBME.2015.2430895
[123]
Haryanto, T., Wasito, I. and Suhartanto, H. (2017) Convolutional Neural Network (CNN) for Gland Images Classification. 2017 11th International Conference on Information & Communication Technology and System (ICTS), Surabaya, 31-31 October 2017, 55-60. https://doi.org/10.1109/ICTS.2017.8265646
[124]
Cao, H., Bernard, S., Heutte, L. and Sabourin, R. (2018) Improve the Performance of Transfer Learning without Fine-Tuning Using Dissimilarity-Based Multi-View Learning for Breast Cancer Histology Images. In: Campilho, A., Karray, F. and ter Haar Romeny, B., Eds., Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science, Vol. 10882, Springer, Cham, 779-787. https://doi.org/10.1007/978-3-319-93000-8_88
[125]
Luo, X., Mori, K. and Peters, T.M. (2018) Advanced Endoscopic Navigation: Surgical Big Data, Methodology, and Applications. Annual Review of Biomedical Engineering, 20, 221-251. https://doi.org/10.1146/annurev-bioeng-062117-120917
[126]
Xiao, C., Choi, E. and Sun, J. (2018) Opportunities and Challenges in Developing Deep Learning Models Using Electronic Health Records Data: A Systematic Review. Journal of the American Medical Informatics Association, 25, 1419-1428. https://doi.org/10.1093/jamia/ocy068
[127]
Shickel, B., Tighe, P.J., Bihorac, A. and Rashidi, P. (2018) Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics, 22, 1589-1604. https://doi.org/10.1109/JBHI.2017.2767063
[128]
Karkra, S., Singh, P. and Kaur, K. (2019) Convolution Neural Network: A Shallow Dive into Deep Neural Net Technology. International Journal of Recent Technology and Engineering (IJRTE), 8, 487-495.
[129]
Ranschaert, E.R., Morozov, S. and Algra, P.R. (2019) Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks. Springer, Berlin. https://doi.org/10.1007/978-3-319-94878-2
[130]
Tsang, G., Xie, X. and Zhou, S.M. (2020) Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges. IEEE Reviews in Biomedical Engineering, 13, 113-129. https://doi.org/10.1109/RBME.2019.2904488
[131]
Haryanto, T., Suhartanto, H., Murni, A. and Kusmardi, K. (2019) Strategies to Improve Performance of Convolutional Neural Network on Histopathological Images Classification. 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS), Bali, Indonesia, 12-13 October 2019, 125-132. https://doi.org/10.1109/ICACSIS47736.2019.8979740
[132]
Das, A., Nair, M.S. and Peter, S.D. (2020) Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: A Review. Journal of Digital Imaging, 33, 1091-1121. https://doi.org/10.1007/s10278-019-00295-z
[133]
Tang, Y., Wang, X., Harrison, A.P., Lu, L., Xiao, J. and Summers, R.M. (2018) Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs. In: Shi, Y., Suk, H.I. and Liu, M., Eds., Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science, Vol. 11046, Springer, Cham, 249-258. https://doi.org/10.1007/978-3-030-00919-9_29
[134]
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. (2014) Generative Adversarial Nets. Proceedings of the 27th International Conference on Neural Information Processing Systems, 2, 2672-2680.
[135]
Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., et al. (2018) Cycada: Cycle-Consistent Adversarial Domain Adaptation. Proceedings of the 35th International Conference on Machine Learning, PMLR, 80, 1989-1998.
[136]
Long, M., Zhu, H., Wang, J. and Jordan, M.I. (2016) Unsupervised Domain Adaptation with Residual Transfer Networks. arXiv: 1602.04433
[137]
Tzeng, E., Hoffman, J., Saenko, K. and Darrell, T. (2017) Adversarial Discriminative Domain Adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 21-26 July 2017, 2962-2971. https://doi.org/10.1109/CVPR.2017.316
[138]
Luo, Y., Zheng, L., Guan, T., Yu, J. and Yang, Y. (2019) Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 15-20 June 2019, 2502-2511. https://doi.org/10.1109/CVPR.2019.00261
[139]
Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H. and Chandraker, M. (2018) Learning to Adapt Structured Output Space for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7472-7481. https://doi.org/10.1109/CVPR.2018.00780
[140]
Liu, D., Zhang, D., Song, Y., Zhang, F., O’Donnell, L., Huang, H., Chen, M. and Cai, W. (2021) PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images. IEEE Transactions on Medical Imaging, 40, 154-165. https://doi.org/10.1109/TMI.2020.3023466
[141]
Ghafoorian, M., et al. (2017) Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D. and Duchesne, S., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, Vol. 10435, Springer, Cham, 516-524.
[142]
Jiang, J., Hu, Y.C., Tyagi, N., Zhang, P., Rimner, A., Mageras, G.S., Deasy, J.O. and Veeraraghavan, H. (2018) Tumor-Aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C. and Fichtinger, G., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, Vol. 11071, Springer, Cham, 777-785. https://doi.org/10.1007/978-3-030-00934-2_86
[143]
Chen, C., Dou, Q., Chen, H. and Heng, P.A. (2018) Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation. In: Shi, Y., Suk, H.I. and Liu, M., Eds., Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science, Vol. 11046, Springer, Cham, 143-151. https://doi.org/10.1007/978-3-030-00919-9_17
[144]
Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M. and Duncan, J.S. (2019) Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation. In: Shen, D., et al., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, Vol. 11765, Springer, Cham, 255-263. https://doi.org/10.1007/978-3-030-32245-8_29
[145]
Zhang, C., Wu, S., Lu, Z., Shen, Y., Wang, J., Huang, P., Lou, J., Liu, C., Xing, L., Zhang, J., Xue, J. and Li, D. (2020) Hybrid Adversarial-Discriminative Network for Leukocyte Classification in Leukemia. Medical Physics, 47, 3732-3744. https://doi.org/10.1002/mp.14144
[146]
Li, C.Y., Liang, X., Hu, Z. and Xing, E.P. (2019) Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 6666-6673. https://doi.org/10.1609/aaai.v33i01.33016666
[147]
Wang, Z., Zhang, J., Feng, J. and Chen, Z. (2014) Knowledge Graph and Text Jointly Embedding. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, October 2014, 1591-1601. https://doi.org/10.3115/v1/D14-1167
[148]
Luo, B.N., Shen, J., Cheng, S.Y., Wang, Y.J. and Pantic, M. (2020) Shape Constrained Network for Eye Segmentation in the Wild. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO, 1-5 March 2020, 1952-1960. https://doi.org/10.1109/WACV45572.2020.9093483
[149]
Wistuba, M., Rawat, A. and Pedapati, T. (2019) A Survey on Neural Architecture Search. arXiv: 1905. 01392.
[150]
Guo, D., Jin, D., Zhu, Z., Ho, T.Y., Harrison, A.P., Chao, C.H., et al. (2020) Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, 13-19 June 2020, 4222-4231. https://doi.org/10.1109/CVPR42600.2020.00428
[151]
Li, S., Wei, J., Chan, H.P., Helvie, M.A., Roubidoux, M.A., Lu, Y., Zhou, C., Hadjiiski, L.M. and Samala, R.K. (2018) Computer-Aided Assessment of Breast Density: Comparison of Supervised Deep Learning and Feature-Based Statistical Learning. Physics in Medicine & Biology, 63, Article ID: 025005. https://doi.org/10.1088/1361-6560/aa9f87
[152]
Carneiro, G., Nascimento, J.C. and Freitas, A. (2012) The Segmentation of the Left Ventricle of the Heart From Ultrasound Data Using Deep Learning Architectures and Derivative-Based Search Methods. IEEE Transactions on Image Processing, 21, 968-982. https://doi.org/10.1109/TIP.2011.2169273
[153]
Xue, Y., Zhang, R., Deng, Y., Chen, K. and Jiang, T. (2017) A Preliminary Examination of the Diagnostic Value of Deep Learning in Hip Osteoarthritis. PLoS ONE, 12, e0178992. https://doi.org/10.1371/journal.pone.0178992
[154]
Chen, C.-M., Huang, Y.-S., Fang, P.-W., Liang, C.-W. and Chang, R.-F. (2020) A Computer-Aided Diagnosis System for Differentiation and Delineation of Malignant Regions on Whole-Slide Prostate Histopathology Image Using Spatial Statistics and Multidimensional DenseNet. Medical Physics, 47, 1021-1033. https://doi.org/10.1002/mp.13964
[155]
Quellec, G., Charrière, K., Boudi, Y., Cochener, B. and Lamard, M. (2017) Deep Image Mining for Diabetic Retinopathy Screening. Medical Image Analysis, 39, 178-193. https://doi.org/10.1016/j.media.2017.04.012
[156]
Saha, S.K., Fernando, B., Cuadros, J., Xiao, D. and Kanagasingam, Y. (2018) Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine. Journal of Digital Imaging, 31, 869-878. https://doi.org/10.1007/s10278-018-0084-9
[157]
Das, A., Rad, P., Choo, K.R., Nouhi, B., Lish, J. and Martel, J. (2019) Distributed Machine Learning Cloud Teleophthalmology IoT for Predicting AMD Disease Progression. Future Generation Computer Systems, 93, 486-498. https://doi.org/10.1016/j.future.2018.10.050
[158]
Kim, Y D., Noh, K.J., Byun, S.J., et al. (2020) Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images. Scientific Reports, 10, Article No. 4623. https://doi.org/10.1038/s41598-020-61519-9
[159]
Betancur, J., Commandeur, F., Motlagh, M., Sharir, T., Einstein, A.J., et al. (2018) Deep Learning for Prediction of Obstructive Disease from Fast Myocardial Perfusion SPECT: A Multicenter Study. JACC: Cardiovascular Imaging, 11, 1654-1663. https://doi.org/10.1016/j.jcmg.2018.01.020
[160]
Chaudhari, A.S., Fang, Z., Kogan, F., Wood, J., Stevens, K.J., Gibbons, E.K., Lee, J.H., Gold, G.E. and Hargreaves, B.A. (2018) Super-Resolution Musculoskeletal MRI Using Deep Learning. Magnetic Resonance in Medicine, 80, 2139-2154. https://doi.org/10.1002/mrm.27178
[161]
Ning, Z., Luo, J., Li, Y., Han, S., Feng, Q., Xu, Y., Chen, W., Chen, T. and Zhang, Y. (2019) Pattern Classification for Gastrointestinal Stromal Tumors by Integration of Radiomics and Deep Convolutional Features. IEEE Journal of Biomedical and Health Informatics, 23, 1181-1191. https://doi.org/10.1109/JBHI.2018.2841992
[162]
Khosravan, N., Celik, H., Turkbey, B., Jones, E.C., Wood, B. and Bagci, U. (2019) A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning. Medical Image Analysis, 51, 101-115. https://doi.org/10.1016/j.media.2018.10.010
[163]
Jang, R., Kim, N., Jang, M., Lee, K.H., Lee, S.M., Lee, K.H., Noh, H.N. and Seo, J.B. (2020) Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images from Multiple Centers. JMIR Medical Informatics, 8, e18089. https://doi.org/10.2196/18089
[164]
Cheng, J.Z., Ni, D., Chou, Y.H., Qin, J., Tiu, C.M., Chang, Y.C., Huang, C.S., Shen, D. and Chen, C.M. (2016) Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Scientific Reports, 6, Article No. 24454. https://doi.org/10.1038/srep24454
[165]
Song, Y., Zhang, Y.D., Yan, X., Liu, H., Zhou, M., Hu, B. and Yang, G. (2018) Computer-Aided Diagnosis of Prostate Cancer Using a Deep Convolutional Neural Network from Multiparametric MRI. Journal of Magnetic Resonance Imaging, 48, 1570-1577. https://doi.org/10.1002/jmri.26047
[166]
Sujit, S.J., Coronado, I., Kamali, A., Narayana, P.A. and Gabr, R.E. (2019) Automated Image Quality Evaluation of Structural Brain MRI Using an Ensemble of Deep Learning Networks. Journal of Magnetic Resonance Imaging, 50, 1260-1267. https://doi.org/10.1002/jmri.26693
[167]
Dar, S.U.H., Yurt, M., Shahdloo, M., Ildız, M.E. and Çukur, T. (2018) Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI. Computer Vision and Pattern Recognition. arXiv: 1805.10704.
