A PROPOSAL OF AN ARTIFICIAL INTELLIGENCE SYSTEM FOR ASSISTANCE IN MAMMOGRAPHY DIAGNOSTICS
DOI:
https://doi.org/10.24867/25BE02JovisicKeywords:
mammography, neural networks, U-net, segmentationAbstract
Mammography as a diagnostic method for detecting malignancy is widely used and relies on the expert interpretation of radiologists. In this paper, the opportunity to improve such a system with artificial intelligence, which would represent support in decision-making related to the interpretation of mammographic images, was explored. The U-net model for tissue segmentation is investigated, its general architecture and architecture adapted for the described problem are described, as well as its application to some of the open datasets. The process and the parameters of the training are described The results of the applied model are presented and discussed and all this is summarized in the concluding chapter.
References
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[4] https://www.kaggle.com/code/theoviel/breast-density-classification-using-monai
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[8] Ciritsis A, Rossi C, Vittoria De Martini I, Eberhard M, Marcon M, Becker AS, et al. Determination of mammographic breast density using a deep convolutional neural network. Br J Radiol 2019; 92: 20180691.
[9] Shen, L., Margolies, L.R., Rothstein, J.H. et al. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci Rep 9, 12495 (2019). https://doi.org/10.1038/s41598-019-48995-4
[10] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick: “Segment Anything”, 2023; [http://arxiv.org/abs/2304.02643 arXiv:2304.02643]
[11] Jun Ma, Bo Wang: “Segment Anything in Medical Images”, 2023; [http://arxiv.org/abs/2304.12306 arXiv:2304.12306]
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[13] https://towardsdatascience.com/active-learning-overview-strategies-and-uncertainty-measures-521565e0b0b
[2] https://www.kaggle.com/competitions/rsna-breast-cancer-detection/
[3] R.N.J. Graham, R.W. Perriss, A.F. Scarsbrook, DICOM demystified: A review of digital file formats and their use in radiological practice, Clinical Radiology, Volume 60, Issue 11, 2005, Pages 1133-1140, ISSN 0009-9260
[4] https://www.kaggle.com/code/theoviel/breast-density-classification-using-monai
[5] Vikash Gupta, Mutlu Demirer, Robert W. Maxwell, Richard D. White, Barbaros Selnur Erdal: “A multi-reconstruction study of breast density estimation using Deep Learning”, 2022; [http://arxiv.org/abs/2202.08238 arXiv:2202.08238]
[6] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich: “Going Deeper with Convolutions”, 2014; [http://arxiv.org/abs/1409.4842 arXiv:1409.4842]
[7] Pablo Fonseca, Julio Mendoza, Jacques Wainer, Jose Ferrer, Joseph Pinto, Jorge Guerrero, Benjamin Castaneda, "Automatic breast density classification using a convolutional neural network architecture search procedure," Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941428 (20 March 2015); doi: 10.1117/12.2081576
[8] Ciritsis A, Rossi C, Vittoria De Martini I, Eberhard M, Marcon M, Becker AS, et al. Determination of mammographic breast density using a deep convolutional neural network. Br J Radiol 2019; 92: 20180691.
[9] Shen, L., Margolies, L.R., Rothstein, J.H. et al. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci Rep 9, 12495 (2019). https://doi.org/10.1038/s41598-019-48995-4
[10] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick: “Segment Anything”, 2023; [http://arxiv.org/abs/2304.02643 arXiv:2304.02643]
[11] Jun Ma, Bo Wang: “Segment Anything in Medical Images”, 2023; [http://arxiv.org/abs/2304.12306 arXiv:2304.12306]
[12] Olaf Ronneberger, Philipp Fischer, Thomas Brox: “U-Net: Convolutional Networks for Biomedical Image Segmentation”, 2015; [http://arxiv.org/abs/1505.04597 arXiv:1505.04597]
[13] https://towardsdatascience.com/active-learning-overview-strategies-and-uncertainty-measures-521565e0b0b
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Published
2023-12-04
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Section
Electrotechnical and Computer Engineering
