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An Integrated Deep Architecture for Lesion Detection in Breast MRI

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Book cover Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

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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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Author information

Authors and Affiliations

  1. National Research Council Canada, Ottawa, ON, K1A 0R6, Canada

    Ghazal Rouhafzay, Chang Shu & Pengcheng Xi

  2. University of Ottawa, Ottawa, ON, K1N 6N5, Canada

    Ghazal Rouhafzay

  3. Carleton University, Ottawa, ON, K1S 5B6, Canada

    Rafik Goubran & Pengcheng Xi

  4. First Affiliated Hospital of Soochow University, Jiangsu, 215006, China

    Yonggang Li

  5. Nantong No. 3 People’s Hospital, Jiangsu, 226000, China

    Haitao Guan

Authors
  1. Ghazal Rouhafzay
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  2. Yonggang Li
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  3. Haitao Guan
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  4. Chang Shu
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  5. Rafik Goubran
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  6. Pengcheng Xi
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Corresponding author

Correspondence to Pengcheng Xi .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

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Rouhafzay, G., Li, Y., Guan, H., Shu, C., Goubran, R., Xi, P. (2020). An Integrated Deep Architecture for Lesion Detection in Breast MRI. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_56

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  • DOI: https://doi.org/10.1007/978-3-030-59830-3_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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