Applying Deep Learning to Medical Imaging: A Review
Source abstract
Abstract
Deep learning (DL) has made significant strides in medical imaging. This review article presents an in-depth analysis of DL applications in medical imaging, focusing on the challenges, methods, and future perspectives. We discuss the impact of DL on the diagnosis and treatment of diseases and how it has revolutionized the medical imaging field. Furthermore, we examine the most recent DL techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their applications in medical imaging. Lastly, we provide insights into the future of DL in medical imaging, highlighting its potential advancements and challenges.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Radiomics and Machine Learning in Medical Imaging · AI in cancer detection · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Key Laboratory of Antennas and Microwave Technology, Xidian University, Xi’an 710071, China
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
Funding
National Natural Science Foundation of China under Grant
References (100+)
Show source-provided references
1. Ayache, N. (2020). Medical Imaging in the Age of Artificial Intelligence. Healthc. Artif. Intell., 89–91.10.1007/978-3-030-32161-1_13 ↗
2. Wang, W., Liang, D., Chen, Q., Iwamoto, Y., Han, X.H., Zhang, Q., Hu, H., Lin, L., and Chen, Y.W. (2020). Medical image classification using deep learning. Deep. Learn. Healthc. Paradig. Appl., 33–51.10.1007/978-3-030-32606-7_3 ↗
3. Deep learning in medical image analysis: A third eye for doctors10.1016/j.jormas.2019.06.002 ↗
4. Deep learning10.1038/nature14539 ↗
5. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
6. U-Net: Convolutional networks for biomedical image segmentation
7. Two-Step Enhanced Deep Learning Approach for Electromagnetic Inverse Scattering Problems10.1109/LAWP.2019.2925578 ↗
8. Enhanced Deep Learning Approach Based on the Deep Convolutional Encod-er-Decoder Architecture for Electromagnetic Inverse Scattering Problems10.1109/LAWP.2020.2995455 ↗
9. Joint inversion of audio-magnetotelluric and seismic travel time data with deep learning constraint10.1109/TGRS.2020.3032743 ↗
10. Enhanced Supervised Descent Learning Technique for Electromagnetic Inverse Scattering Problems by the Deep Convolutional Neural Networks10.1109/TAP.2022.3196496 ↗
11. Enhanced PML Based on the Long Short Term Memory Network for the FDTD Method10.1109/ACCESS.2020.2969569 ↗
12. Implementing the Fast Full-Wave Electromagnetic Forward Solver Using the Deep Convolutional Encoder-Decoder Architecture10.1109/TAP.2022.3216920 ↗
13. Solving Electromagnetic Inverse Scattering Problems in Inhomogeneous Media by Deep Convolutional Encoder–Decoder Structure10.1109/TAP.2023.3239185 ↗
14. Enhanced Two-Step Deep-Learning Approach for Electromagnetic-Inverse-Scattering Problems: Frequency Extrapolation and Scatterer Reconstruction10.1109/TAP.2022.3225532 ↗
15. Fast Full Wave Electromagnetic Forward Solver Based on Deep Conditional Convolutional Autoencoders10.1109/LAWP.2022.3224983 ↗
16. Deep Long Short-Term Memory Networks-Based Solving Method for the FDTD Method: 2-D Case10.1109/LMWT.2022.3223959 ↗
17. Machine-Learning-Based PML for the FDTD Method10.1109/LAWP.2018.2885570 ↗
18. Yao, H., Zhang, L., Yang, H., Li, M., and Zhang, B. (2022). Snow Parameters Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks. Sensors, 22.10.3390/s22134769 ↗
19. Applying Convolutional Neural Networks for The Source Reconstruction10.2528/PIERM18082907 ↗
20. Applying Deep Learning Approach to the Far-Field Subwavelength Imaging Based on Near-Field Resonant Metalens at Microwave Frequencies10.1109/ACCESS.2019.2915263 ↗
21. Embedding the behavior macromodel into TDIE for transient field-circuit simulations10.1109/TAP.2016.2560901 ↗
22. Transient Heterogeneous Electromagnetic Simulation with DGTD and Behavioral Macromodel10.1109/TEMC.2016.2642955 ↗
23. Flexible Wideband Microstrip-Slotline-Microstrip Power Divider and Its Application to Antenna Array10.1109/ACCESS.2019.2944462 ↗
24. A Low-Profile Wideband CP End-Fire Magnetoelectric Antenna Using Dual-Mode Resonances10.1109/TAP.2019.2911399 ↗
25. Yao, H.M., Jiang, L., Zhang, H.H., and Wei, E.I. (2019, January 10–13). Machine learning methodology review for computational electromagnetics. Proceedings of the 2019 International Applied Computational Electromagnetics Society Symposium-China (ACES), Washington, DC, USA.10.23919/ACES48530.2019.9060439 ↗
26. Guo, R., Li, M., Yang, F., Yao, H., Jiang, L., Ng, M., and Abubakar, A. (2020, January 11–16). Joint 2D inversion of AMT and seismic traveltime data with deep learning constraint. Proceedings of the SEG International Exposition and Annual Meeting, Virtual.10.1190/segam2020-3426298.1 ↗
27. Yao, H.M., Jiang, L.J., and Qin, Y.W. (2017, January 9–14). Machine learning based method of moments (ML-MoM). Proceedings of the 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, San Diego, CA, USA.10.1109/APUSNCURSINRSM.2017.8072529 ↗
28. Yao, H.M., Qin, Y.W., and Jiang, L.J. (2016, January 14–16). Machine learning based MoM (ML-MoM) for parasitic capacitance extractions. Proceedings of the 2016 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS), Honolulu, HI, USA.10.1109/EDAPS.2016.7893155 ↗
29. Yao, H.M., and Jiang, L.J. (2018, January 8–13). Machine learning based neural network solving methods for the FDTD method. Proceedings of the 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Boston, MA, USA.10.1109/APUSNCURSINRSM.2018.8608745 ↗
30. Jiang, L., Yao, H., Zhang, H., and Qin, Y. (2018, January 26–28). Machine Learning Based Computational Electromagnetic Analysis for Electromagnetic Compatibility. Proceedings of the 2018 IEEE International Conference on Computational Electromagnetics (ICCEM), Chengdu, China.10.1109/COMPEM.2018.8496540 ↗
31. Yao, H.M., Jiang, L.J., and Wei, E.I. (2019, January 3–7). Source Reconstruction Method based on Machine Learning Algorithms. Proceedings of the 2019 Joint International Symposium on Electromagnetic Compatibility, Sapporo and Asia-Pacific International Symposium on Electromagnetic Compatibility (EMC Sapporo/APEMC), Sapporo, Japan.10.23919/EMCTokyo.2019.8893747 ↗
32. Zhang, H.H., Yao, H.M., and Jiang, L.J. (2015, January 25–28). Novel time domain integral equation method hybridized with the macromodels of circuits. Proceedings of the 2015 IEEE 24th Electrical Performance of Electronic Packaging and Systems (EPEPS), San Jose, CA, USA.10.1109/EPEPS.2015.7347147 ↗
33. Zhang, H.H., Jiang, L.J., Yao, H.M., and Zhang, Y. (2016, January 25–29). Coupling DGTD and behavioral macromodel for transient heterogeneous electromagnetic simulations. Proceedings of the 2016 IEEE International Symposium on Electromagnetic Compatibility (EMC), Ottawa, ON, Canada.10.1109/ISEMC.2016.7571646 ↗
34. Zhang, H.H., Jiang, L.J., Yao, H.M., Zhao, X.W., and Zhang, Y. (2016, January 8–11). Hybrid field-circuit simulation by coupling DGTD with behavioral macromodel. Proceedings of the 2016 Progress in Electromagnetic Research Symposium (PIERS), Shanghai, China.
35. Modelling electrical conduction in nanostructure assemblies through complex networks10.1038/s41563-020-0664-1 ↗
36. Characterizing percolative materials by straining10.1039/C8NR09120J ↗
37. Direct growth of single-metal-atom chains10.1038/s44160-022-00038-z ↗
38. Liu, H., Yao, H., and Feng, L. (2013, January 7–10). A nanometer-resolution displacement measurement system based on laser feedback interferometry. Proceedings of the 8th Annual IEEE International Conference on Nano/Micro Engineered and Molecular Systems, Xiamen, China.10.1109/NEMS.2013.6559953 ↗
39. Simulation and Error Analysis of a Laser Feedback Interference System Based on Phase-freezing Technology
40. Lateral Two-Dimensional Material Heterojunction Photodetectors with Ultrahigh Speed and Detectivity10.1021/acsami.8b19093 ↗
41. Enhancing the Sensitivity of Percolative Graphene Films for Flexible and Transparent Pressure Sensor Arrays10.1002/adfm.201503674 ↗
42. Yao, H.M., Li, M., Jiang, L., and Ng, M. (2023). Antenna Array Diagnosis by Using Deep Learning Approach. IEEE Trans. Antennas Propag., early access.
43. Yao, H.M., Jiang, L., and Ng, M. (2023). Enhanced Deep Learning Approach Based on the Conditional Generative Adversarial Network for Electromagnetic Inverse Scattering Problems. IEEE Trans. Antennas Propag., early access.
44. Deep learning in medical image analysis10.1146/annurev-bioeng-071516-044442 ↗
45. A survey on deep learning in medical image analysis10.1016/j.media.2017.07.005 ↗
46. An overview of deep learning in medical imaging10.1016/j.imu.2021.100723 ↗
47. Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Sys-tematic Review
48. Deep Convolutional Neural Network–Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study10.2196/27468 ↗
49. A fast and efficient CNN model for B-ALL diagnosis and its subtypes classification using peripheral blood smear images10.1002/int.22753 ↗
50. X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Para-digm During the Pandemic10.1155/2021/9942873 ↗
51. Ghaderzadeh, M., and Aria, M. (2021, January 14–16). Management of COVID-19 Detection Using Artificial Intelligence in 2020 Pandemic. Proceedings of the 5th International Conference on Medical and Health Informatics, Kyoto, Japan.10.1145/3472813.3472820 ↗
52. Gheisari, M., Ebrahimzadeh, F., Rahimi, M., Moazzamigodarzi, M., Liu, Y., Pramanik, P.K.D., Heravi, M.A., Mehbodniya, A., Ghaderzadeh, M., and Feylizadeh, M.R. (2023). Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey. CAAI Trans. Intell. Technol., early view.10.1049/cit2.12180 ↗
53. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
54. A comprehensive survey on deep learning-based approaches for medical image analysis
55. Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv.
56. An overview of medical imaging modalities: The role of imaging physics in medical education
57. A. C. Society (2023, May 23). Imaging (Radiology) Tests, American Cancer Society. Available online: https://www.cancer.org/treatment/understanding-your-diagnosis/tests/imaging-radiology-tests-for-cancer.html.
58. Simpson, A.L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., Van Ginneken, B., Kopp-Schneider, A., Landman, B.A., Litjens, G., and Menze, B. (2019). A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv.
59. Big data, big problems: Emerging issues in the ethics of data science and journalism10.1080/08900523.2014.863126 ↗
60. The Mythos of Model Interpretability10.1145/3236386.3241340 ↗
61. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22–29). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.74 ↗
62. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
63. Imagenet classification with deep convolutional neural networks10.1145/3065386 ↗
64. Luo, X., Hu, M., Song, T., Wang, G., and Zhang, S. (2022, January 5–6). Semi-supervised medical image segmentation via cross teaching between CNN and transformer. Proceedings of the International Conference on Medical Imaging with Deep Learning, PMLR, Durham, NC, USA.
65. CNN Based Multiclass Brain Tumor Detection Using Medical Imaging10.1155/2022/1830010 ↗
66. Srikantamurthy, M.M., Rallabandi, V.P., Dudekula, D.B., Natarajan, S., and Park, J. (2023). Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning. BMC Med. Imaging, 23.10.1186/s12880-023-00964-0 ↗
67. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
68. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
69. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique10.1109/TMI.2016.2553401 ↗
70. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs10.1109/TPAMI.2017.2699184 ↗
71. A survey on transfer learning10.1109/TKDE.2009.191 ↗
72. Evaluation of COVID-19 Reported Statistical Data Using Cooperative Convolutional Neural Network Model (CCNN)10.3390/covid2050051 ↗
73. Li, Z., Zhang, H., Li, Z., and Ren, Z. (2022). Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation. Appl. Sci., 12.10.3390/app12147149 ↗
74. Safarov, S., and Whangbo, T.K. (2021). A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution. Sensors, 21.10.21203/rs.3.rs-158417/v1 ↗
75. Khan, S., Rahmani, H., Shah, S.A.A., and Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision, Springer.10.1007/978-3-031-01821-3 ↗
76. Convolutional neural networks for medical image analysis: Full training or fine tuning?10.1109/TMI.2016.2535302 ↗
77. Dermatologist-level classification of skin cancer with deep neural networks10.1038/nature21056 ↗
78. Goodfellow, I.J., Shlens, J., and Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv.
79. Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., and Price, S.J. (2012, January 1–5). Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR. Proceedings of the MICCAI 2012, Nice, France.10.1007/978-3-642-33454-2_46 ↗
80. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
81. Cho, K., Van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.10.3115/v1/W14-4012 ↗
82. Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., and Darrell, T. (2014, January 23–28). Long-term Recurrent Convolutional Networks for Visual Recognition and Description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.10.21236/ADA623249 ↗
83. Optimal Medical Image Size Reduction Model Creation Using Recurrent Neural Network and GenPSOWVQ
84. Pyramid Convolutional RNN for MRI Image Reconstruction10.1109/TMI.2022.3153849 ↗
85. A review on deep learning in medical image analysis10.1007/s13735-021-00218-1 ↗
86. Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks10.1109/TMI.2016.2536809 ↗
87. Yang, K., Mohammed, E.A., and Far, B.H. (2021, January 10–12). Detection of Alzheimer’s Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images. Proceedings of the 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA.10.1109/IRI51335.2021.00051 ↗
88. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 21–26). ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.369 ↗
89. Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.J., and Fei-Fei, L. (2018, January 18–23). Thoracic disease identification and localization with limited supervision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00865 ↗
90. Padoy, N. (2012, January 1–5). Towards automatic recognition of surgical activities. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Nice, France.
91. Mutter, V., Gangi, A., and Rekik, M.A. (2019). Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, Springer.
92. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Advances in Neural Information Processing Systems, Springer.
93. Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22–29). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.244 ↗
94. Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., and Langs, G. (2017, January 25–30). Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. Proceedings of the International Conference on Information Processing in Medical Imaging, Boone, NC, USA.10.1007/978-3-319-59050-9_12 ↗
95. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion clas-sification10.1016/j.neucom.2018.09.013 ↗
96. MR-based synthetic CT generation using a deep convolutional neural network method10.1002/mp.12155 ↗
97. Generative adversarial network in medical imaging: A review10.1016/j.media.2019.101552 ↗
98. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A.P., Tejani, A., Totz, J., and Wang, Z. (2017, January 21–26). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.19 ↗
99. Chen, Y., Xie, Y., Zhou, Z., Shi, F., Christodoulou, A.G., and Li, D. (2018, January 4–7). Brain MRI super resolution using 3D deep densely connected neural networks. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.10.1109/ISBI.2018.8363679 ↗
100. Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., and Choo, J. (2018, January 18–23). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00916 ↗