| DOI | Resolve DOI: https://doi.org/10.1007/978-3-030-59830-3_56 |
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| Author | Search for: Rouhafzay, Ghazal1ORCID identifier: https://orcid.org/0000-0003-3762-0900; Search for: Li, YonggangORCID identifier: https://orcid.org/0000-0001-8974-3094; Search for: Guan, HaitaoORCID identifier: https://orcid.org/0000-0001-5056-120X; Search for: Shu, Chang1ORCID identifier: https://orcid.org/0000-0001-6331-0522; Search for: Goubran, RafikORCID identifier: https://orcid.org/0000-0003-4087-416X; Search for: Xi, Pengcheng1ORCID identifier: https://orcid.org/0000-0003-3236-5234 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Book Chapter |
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| Conference | International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2020), October 19-23, 2020, Zhongshan, China |
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| Subject | Magnetic Resonance Imaging; breast lesion detection; deep learning; deep CNN; medical CAEs |
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| Abstract | Complex nature of medical images and tedious process of data exploration calls for the development of Computer Aided Detection (CADe) methods to ease the process of lesion detection. Recent deep learning-based object detectors from computer vision are adapted to the creation of CADe lesion detectors. This research starts with state-of-the-art object detectors, namely Faster R-CNN, YOLO v2 and Grad-CAM, to determine the location of lesions in Magnetic Resonance Images of breast. A series of experiments are conducted to find the best set up for maximizing the Average Precision (AP) of each method. Consequently, AP values of 0.6993 and 0.7651 are obtained for Faster R-CNN and YOLO v2 respectively. Taking into consideration the pros and cons of each method, we propose different integration architectures in order to overcome the shortcomings of each algorithm, hence enhancing the overall lesion detection performance. The integrated architectures succeed to obtain an AP value up to 0.8097 while providing explainable reasoning that is essential for medical CADe. |
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| Publication date | 2020-10-09 |
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| Publisher | Springer |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | bdfaadb4-360b-4eaf-9c5a-1e3e4c012e62 |
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| Record created | 2020-11-02 |
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| Record modified | 2022-02-21 |
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