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A Synopsis of Machine and Deep Learning in Medical Physics and Radiology

Year 2022, Volume: 6 Issue: 3, 946 - 957, 29.09.2022

Zohal Emam Emel Ada

https://doi.org/10.30621/jbachs.960154

Abstract

Machine learning (ML) and deep learning (DL) technologies introduced in the fields of medical physics, radiology, and oncology have made great strides in the past few years. A good many applications have proven to be an efficacious automated diagnosis and radiotherapy system. This paper outlines DL's general concepts and principles, key computational methods, and resources, as well as the implementation of automated models in diagnostic radiology and radiation oncology research. In addition, the potential challenges and solutions of DL technology are also discussed.

Keywords

Deep learning Machine learning Radiology Oncology

Supporting Institution

Health Science Institute, Dokuz Eylul University

References

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Year 2022, Volume: 6 Issue: 3, 946 - 957, 29.09.2022

Zohal Emam Emel Ada

https://doi.org/10.30621/jbachs.960154

Abstract

References

  • Fouad F. The Fourth Industrial Revolution is the AI Revolution an Academy Prospective. IJISCS. 2019;8(5):155-67. doi: 10.30534/ijiscs/2019/01852019
  • Choy G, Khalilzadeh O, Michalski M, et al. Current Applications and Future Impact of Machine Learning in Radiology. Radiology. 2018;288(2):318-28. doi: 10.1148/radiol.2018171820
  • Sahiner B, Pezeshk A, Hadjiiski LM, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46(1): e1-e36. doi: 10.1002/mp.13264
  • Avanzo M, Trianni A, Botta F, Talamonti C, Stasi M, Iori M. Artificial Intelligence and the Medical Physicist: Welcome to the Machine. Appl Sci. 2021 Jan;11(4):1691. doi: 10.3390/app11041691
  • Shen C, Nguyen D, Zhou Z, Jiang SB, Dong B, Jia X. An introduction to deep learning in medical physics: advantages, potential, and challenges. Phys Med Biol. 2020;65:05TR1. doi: 10.1088/1361-6560/ab6f51
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  • Baraniuk R, Donoho D, Gavish M. The science of deep learning. Proc Natl Acad Sci. U S A. 2020;117(48):30029-32. doi: 10.1073/pnas.2020596117
  • Sutton RS, Barto AG. Reinforcement learning: An introduction. 2nd ed. Cambridge MA: MIT press. 2018.
  • Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP. Deep Learning in Neuroradiology. AJNR Am J Neuroradiol. 2018;39(10):1776-84. doi:10.3174/ajnr.A5543
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  • Lecun Y, Bengio Y. Convolutional networks for images, speech, and time-series. In Arbib MA, editor, The handbook of brain theory and neural networks. MIT Press. 1995.
  • Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. InInternational Conference on Medical image computing and computer-assisted intervention. Springer, Cham. 2015 Oct 5;234-41. doi: 10.1007/978-3-319-24574-4_28
  • Milletari, F., Navab, N., Ahmadi, S.A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 3DV. IEEE. 2016;565–71. doi: 10.1109/3DV.2016.79
  • Hochreiter S, Schmidhuber J. LSTM can solve hard long time lag problems. NIPS. 1997;473-9. https://dl.acm.org/doi/10.5555/3104482.3104587
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  • Salakhutdinov R, Hinton G. Deep boltzmann machines. In Artificial intelligence and statistics. PMLR. 2009 Apr 15;5:448-55. http://proceedings.mlr.press/v5/salakhutdinov09a.html
  • Hinton G. Boltzmann Machines. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. 2011. doi: 10.1007/978-0-387-30164-8_83
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  • Yeung M, Sala E, Schönlieb CB, Rundo L. Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation. arXiv preprint arXiv:2102.04525. 2021 Feb 8. https://arxiv.org/abs/2102.04525
  • Haji SH, Abdulazeez AM. COMPARISON OF OPTIMIZATION TECHNIQUES BASED ON GRADIENT DESCENT ALGORITHM: A REVIEW. PalArch's Journal of Archaeology of Egypt/Egyptology. 2021 Feb 18;18(4):2715-43. https://www.archives.palarch.nl/index.php/jae/article/view/6705
  • Abadi M, Barham P, Chen J, et al. Tensorflow: A system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), USENIX Association. 2016;265-83. https://arxiv.org/abs/1605.08695
  • Chollet, F. Keras: Deep learning for humans. GitHub. 2015. Retrieved [2021]. Available from: https://github.com/keras-team/keras
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  • Jia Y, Shelhamer E, Donahue J, et al. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of ACM Multimedia. 2014;675–8. doi: 10.1145/2647868.2654889
  • MATLAB®, version 9.10.0.1613233 (R2021a). The Mathworks, Inc. Natick, MA. 2021. https://www.mathworks.com/
  • Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging. 2013 Dec 1;26(6):1045-57. doi: 10.1007/s10278-013-9622-7
  • Tomczak,K., Czerwinska,P. and Wiznerowicz,M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol. Pozn. 2015;19(1A):A68–A77. doi: 10.5114/wo.2014.47136
  • Liu X, Song L, Liu S, Zhang Y. A review of deep-learning-based medical image segmentation methods. Sustainability. 2021 Jan;13(3):1224. doi: 10.3390/su13031224
  • Dalmış MU, Litjens G, Holland K, et al. Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Med. Phys. 2017;44(2):533-46. doi: 10.1002/mp.12079
  • Nie D, Wang L, Trullo R, et al. Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework. In: Wang Q., Shi Y., Suk HI., Suzuki K. (eds). MLMI. Lecture Notes in Computer Science. Springer. Cham. 2017;10541:266-73. doi: 10.1007/978-3-319-67389-9_31
  • Cheng R, Roth HR, Lay N, et al. Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks. J. Med. Imaging 2017; 4:041302. doi: 10.1117/1.jmi.4.4.041302
  • Avanzo M, Wei L, Stancanello J, et al. Machine and deep learning methods for radiomics. Med. Phys. 2020;47(5): e185-e202. doi: 10.1002/mp.13678
  • Gao M, Huang S, Pan X, Liao X, Yang R, Liu J. Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas. Front Oncol. 2020; 10:1676. doi: 10.3389/fonc.2020.01676
  • Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res. 2021;23(4): e22394. doi: 10.2196/22394
  • Mitra S. Deep Learning with Radiogenomics towards Personalized Management of Gliomas. IEEE Rev Biomed Eng. Epub ahead of print. 2012; PMID: 33900921. doi: 10.1109/RBME.2021.3075500
  • Smedley NF, Aberle DR, Hsu W. Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer. J Med Imaging (Bellingham). 2021;8(3):031906. doi: 10.1117/1.jmi.8.3.031906
  • Siar M, Teshnehlab M, editors. Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm. ICCKE. 2019;363-8. doi: 10.1109/iccke48569.2019.8964846
  • Zhen SH, Cheng M, Tao YB, Wang YF, Juengpanich S, Jiang ZY, et al. Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data. Front Oncol. 2020; 10:680. doi: 10.3389/fonc.2020.00680
  • Jojoa Acosta MF, Caballero Tovar LY, Garcia-Zapirain MB, Percybrooks WS. Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Med Imaging. 2021;21(1):6. doi: 10.1186/s12880-020-00534-8
  • Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI. 2017 May; 3462–71. doi: 10.1109/cvpr.2017.
  • Halder A, Dey D, Sadhu AK. Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review. J Digit Imaging. 2020;33(3):655-77. doi: 10.1007/s10278-020-00320-6
  • Dou Q, Chen H, Yu L, et al. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging. 2016;35(5):1182-95. doi: 10.1109/tmi.2016.2528129
  • Yang W, Chen Y, Liu Y, et al. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Med. Image Anal. 2017; 35:421-33. doi: 10.1016/j.media.2016.08.004
  • Xiang L, Qiao Y, Nie D, et al. Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI. Neurocomputing. 2017; 267:406-16. doi: 10.1016/j.neucom.2017.06.048. doi: 10.1016/j.neucom.2017.06.048
  • Fu J, Yang Y, Singhrao K, et al. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging. Med Phys. 2019;46(9):3788-98. doi: 10.1002/mp.13672
  • Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging. Radiology. 2018;286(2):676-84. doi: 10.1148/radiol.2017170700
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There are 68 citations in total.

Details

Primary Language English
Subjects Health Care Administration
Journal Section Reviews
Authors

Zohal Emam 0000-0003-1601-7424

Emel Ada This is me 0000-0002-0463-0945

Publication Date September 29, 2022
Submission Date August 9, 2021
Published in Issue Year 2022 Volume: 6 Issue: 3

Cite

APA Emam, Z., & Ada, E. (2022). A Synopsis of Machine and Deep Learning in Medical Physics and Radiology. Journal of Basic and Clinical Health Sciences, 6(3), 946-957. https://doi.org/10.30621/jbachs.960154
AMA Emam Z, Ada E. A Synopsis of Machine and Deep Learning in Medical Physics and Radiology. JBACHS. September 2022;6(3):946-957. doi:10.30621/jbachs.960154
Chicago Emam, Zohal, and Emel Ada. “A Synopsis of Machine and Deep Learning in Medical Physics and Radiology”. Journal of Basic and Clinical Health Sciences 6, no. 3 (September 2022): 946-57. https://doi.org/10.30621/jbachs.960154.
EndNote Emam Z, Ada E (September 1, 2022) A Synopsis of Machine and Deep Learning in Medical Physics and Radiology. Journal of Basic and Clinical Health Sciences 6 3 946–957.
IEEE Z. Emam and E. Ada, “A Synopsis of Machine and Deep Learning in Medical Physics and Radiology”, JBACHS, vol. 6, no. 3, pp. 946–957, 2022, doi: 10.30621/jbachs.960154.
ISNAD Emam, Zohal - Ada, Emel. “A Synopsis of Machine and Deep Learning in Medical Physics and Radiology”. Journal of Basic and Clinical Health Sciences 6/3 (September 2022), 946-957. https://doi.org/10.30621/jbachs.960154.
JAMA Emam Z, Ada E. A Synopsis of Machine and Deep Learning in Medical Physics and Radiology. JBACHS. 2022;6:946–957.
MLA Emam, Zohal and Emel Ada. “A Synopsis of Machine and Deep Learning in Medical Physics and Radiology”. Journal of Basic and Clinical Health Sciences, vol. 6, no. 3, 2022, pp. 946-57, doi:10.30621/jbachs.960154.
Vancouver Emam Z, Ada E. A Synopsis of Machine and Deep Learning in Medical Physics and Radiology. JBACHS. 2022;6(3):946-57.
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