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How to Build Artificial Intelligence Algorithms for Imaging Applications

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Artificial Intelligence in Cardiothoracic Imaging

Part of the book series: Contemporary Medical Imaging ((CMI))

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Abstract

Recent fervor surrounding the use of artificial intelligence (AI) for radiology applications has been driven by the use of convolutional neural networks to greatly improve performance on a variety of imaging tasks. In this chapter, we give an overview of how to build an AI algorithm for imaging purposes with a focus on convolutional neural networks. We will discuss common types of imaging tasks, basic convolutional neural network components, common neural network architectures used for each type of imaging task, loss functions, and basics of training. We also discuss several more advanced concepts of algorithm design in addition to selection of deep learning libraries and hardware.

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Notes

  1. 1.

    Also called a feature map.

  2. 2.

    While this is the original formulation of a convolutional layer, in many cases, a padding operation is now performed, for instance, by adding zeros on the edges of the input images, so that the output has the same dimensions as the input.

  3. 3.

    This is known as a regression problem, in which the output is a number rather than a class.

References

  1. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324. https://doi.org/10.1109/5.726791.

    Article  Google Scholar 

  2. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, vol. 1. Lake Tahoe, Nevada: Curran Associates Inc.; 2012. p. 1097–105.

    Google Scholar 

  3. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A, editors. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

    Google Scholar 

  4. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014.

    Google Scholar 

  5. He K, Zhang X, Ren S, Sun J, editors. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

    Google Scholar 

  6. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ, editors. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

    Google Scholar 

  7. Xie S, Girshick R, Dollár P, Tu Z, He K, editors. Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. 21–26 July 2017.

    Google Scholar 

  8. Hu J, Shen L, Sun G, editors. Squeeze-and-excitation networks. 2018 IEEE/CVF Conference on computer vision and pattern recognition. 2018. 18–23 June 2018.

    Google Scholar 

  9. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–82. https://doi.org/10.1148/radiol.2017162326. Epub 2017/04/25. PubMed PMID: 28436741.

    Article  PubMed  Google Scholar 

  10. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging. 2016;35(5):1207–16. https://doi.org/10.1109/TMI.2016.2535865.

    Article  PubMed  Google Scholar 

  11. Tang Y-X, Tang Y-B, Peng Y, Yan K, Bagheri M, Redd BA, Brandon CJ, Lu Z, Han M, Xiao J. Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ Digit Med. 2020;3(1):1–8.

    Article  Google Scholar 

  12. Girshick R, Donahue J, Darrell T, Malik J, editors. Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE conference on computer vision and pattern recognition. 2014. 23–28 June 2014.

    Google Scholar 

  13. Girshick R, editor. Fast R-CNN. 2015 IEEE International conference on computer vision (ICCV). 2015. 7–13 Dec 2015.

    Google Scholar 

  14. Ren S, He K, Girshick R, Sun J, editors. Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. 2015;28:1–9.

    Google Scholar 

  15. Redmon J, Divvala S, Girshick R, Farhadi A, editors. You only look once: unified, real-time object detection. In: 2016 IEEE Conference on computer vision and pattern recognition (CVPR). 2016. 27–30 June 2016.

    Google Scholar 

  16. Redmon J, Farhadi A, editors. YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on computer vision and pattern recognition (CVPR). 2017. 21–26 July 2017.

    Google Scholar 

  17. Redmon J, Farhadi A. Yolov3: an incremental improvement. arXiv preprint arXiv:180402767. 2018.

    Google Scholar 

  18. Santosh KC, Dhar MK, Rajbhandari R, Neupane A, editors. Deep neural network for foreign object detection in chest X-rays. In: 2020 IEEE 33rd International symposium on computer-based medical systems (CBMS). 2020. 28–30 July 2020.

    Google Scholar 

  19. Sindhu Ramachandran S, George J, Skaria S, Varun VV. “Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans,” Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751I (27 February 2018); https://doi.org/10.1117/12.2293699. Event: SPIE Medical Imaging, 2018, Houston, Texas, United States.

  20. Traoré A, Ly AO, Akhloufi MA, editors. Evaluating deep learning algorithms in pulmonary nodule detection*. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020. 20–24 July 2020.

    Google Scholar 

  21. Cho Y, Lee SM, Cho YH, Lee JG, Park B, Lee G, Kim N, Seo JB. Deep chest X-ray: detection and classification of lesions based on deep convolutional neural networks. Int J Imaging Syst Technol. 2021;31(1):72–81.

    Google Scholar 

  22. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):640–51. https://doi.org/10.1109/TPAMI.2016.2572683.

    Article  PubMed  Google Scholar 

  23. Long J, Shelhamer E, Darrell T, editors. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

    Google Scholar 

  24. Ronneberger O, Fischer P, Brox T, editors. U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer; New York, 2015.

    Google Scholar 

  25. He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In: International Conference on Computer Vision (ICCV). 2017.

    Google Scholar 

  26. Tao Q, Yan W, Wang Y, Paiman EH, Shamonin DP, Garg P, Plein S, Huang L, Xia L, Sramko M. Deep learning–based method for fully automatic quantification of left ventricle function from cine MR images: a multivendor, multicenter study. Radiology. 2019;290(1):81–8.

    Article  PubMed  Google Scholar 

  27. Hahn LD, Mistelbauer G, Higashigaito K, Koci M, Willemink MJ, Sailer AM, Fischbein M, Fleischmann D. CT-based true-and false-lumen segmentation in Type B aortic dissection using machine learning. Radiol Cardiothorac Imaging. 2020;2(3):e190179.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Fahmy AS, Neisius U, Chan RH, Rowin EJ, Manning WJ, Maron MS, Nezafat R. Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: a multicenter multivendor study. Radiology. 2020;294(1):52–60.

    Article  PubMed  Google Scholar 

  29. Zhang R, Cheng C, Zhao X, Li X. Multiscale mask R-CNN–based lung tumor detection using PET imaging. Mol Imaging. 2019;18:1536012119863531.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Yang Y, Sun J, Li H, Xu Z. ADMM-CSNet: a deep learning approach for image compressive sensing. IEEE Trans Pattern Anal Mach Intell. 2020;42(3):521–38.

    Google Scholar 

  31. Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, Zhou J, Wang G. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging. 2017;36(12):2524–35. https://doi.org/10.1109/TMI.2017.2715284. Epub 2017/06/18. PubMed PMID: 28622671; PMCID: PMC5727581.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Jin KH, McCann MT, Froustey E, Unser M. Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process. 2017;26(9):4509–22.

    Article  Google Scholar 

  33. Zhao H, Gallo O, Frosio I, Kautz J. Loss functions for image restoration with neural networks. IEEE Trans Comput Imaging. 2017;3(1):47–57. https://doi.org/10.1109/TCI.2016.2644865.

    Article  Google Scholar 

  34. Bahrami N, Retson T, Blansit K, Wang K, Hsiao A. Automated selection of myocardial inversion time with a convolutional neural network: spatial temporal ensemble myocardium inversion network (STEMI-NET). Magn Reson Med. 2019;81(5):3283–91. https://doi.org/10.1002/mrm.27680. Epub 2019/02/05. PubMed PMID: 30714197.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cui S, Ming S, Lin Y, Chen F, Shen Q, Li H, Chen G, Gong X, Wang H. Development and clinical application of deep learning model for lung nodules screening on CT images. Sci Rep. 2020;10(1):13657. https://doi.org/10.1038/s41598-020-70629-3. Epub 2020/08/14. PubMed PMID: 32788705; PMCID: PMC7423892.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Tan J, Huo Y, Liang Z, Li L. Expert knowledge-infused deep learning for automatic lung nodule detection. J Xray Sci Technol. 2019;27(1):17–35. https://doi.org/10.3233/XST-180426. Epub 2018/11/20. PubMed PMID: 30452432; PMCID: PMC6453714.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Blansit K, Retson T, Masutani E, Bahrami N, Hsiao A. Deep learning–based prescription of cardiac MRI planes. Radiol Artif Intell. 2019;1(6):e180069. https://doi.org/10.1148/ryai.2019180069.

    Article  PubMed  PubMed Central  Google Scholar 

  38. de Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, Isgum I. A deep learning framework for unsupervised affine and deformable image registration. Med Image Anal. 2019;52:128–43. https://doi.org/10.1016/j.media.2018.11.010. Epub 2018/12/24. PubMed PMID: 30579222.

    Article  PubMed  Google Scholar 

  39. Szeliski R. Computer vision: algorithms and applications. Springer Science & Business Media; New York, 2010.

    Google Scholar 

  40. Chollet F. Deep learning with Python. Manning Publications Company; Shelter Island, NY, 2017.

    Google Scholar 

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Hahn, L., Masutani, E., Hasenstab, K. (2022). How to Build Artificial Intelligence Algorithms for Imaging Applications. In: De Cecco, C.N., van Assen, M., Leiner, T. (eds) Artificial Intelligence in Cardiothoracic Imaging. Contemporary Medical Imaging. Humana, Cham. https://doi.org/10.1007/978-3-030-92087-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-92087-6_6

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