Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Preprint English OPEN
Xi, Pengcheng; Shu, Chang; Goubran, Rafik;
(2018)
  • Subject: Computer Science - Computer Vision and Pattern Recognition

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in m... View more
  • References (17)
    17 references, page 1 of 2

    [1] “Breast cancer: prevention and control,” http://www.who.int/cancer/ detection/breastcancer/en/, accessed: 2018-02-13.

    [2] “Acr bi-rads-mammography, ultrasound and magnetic resonance imaging,” 4th ed., American College of Radiology, 2003.

    [3] E. M. Alkabawi, A. R. Hilal, and O. A. Basir, “Computer-aided classification of multi-types of dementia via convolutional neural networks,” in 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), May 2017, pp. 45-50.

    [21] P. Rajpurkar, J. Irvin, A. Bagul, D. Ding, T. Duan, H. Mehta, B. Yang, K. Zhu, D. Laird, R. L. Ball, C. Langlotz, K. Shpanskaya, M. P. Lungren, and A. Ng, “Mura dataset: Towards radiologist-level abnormality detection in musculoskeletal radiographs,” arXiv, 2017. [Online]. Available: https://arxiv.org/abs/1712.06957v3

    [22] P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya, M. P. Lungren, and A. Y. Ng, “Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning,” CoRR, vol. abs/1711.05225, 2017. [Online]. Available: http://arxiv.org/abs/1711.05225

    [23] S. Rosati, V. Giannini, C. Castagneri, D. Regge, and G. Balestra, “Dataset homogeneity assessment for a prostate cancer cad system,” in 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA), May 2016, pp. 1-7.

    [24] V. C. C. Roza, A. M. de Almeida, and O. A. Postolache, “Design of an artificial neural network and feature extraction to identify arrhythmias from ecg,” in 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), May 2017, pp. 391-396.

    [25] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV), vol. 115, no. 3, pp. 211-252, 2015.

    [26] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR, vol. abs/1409.1556, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556

    [27] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Computer Vision and Pattern Recognition (CVPR), 2015. [Online]. Available: http://arxiv.org/abs/1409.4842

  • Related Organizations (3)
  • Metrics
Share - Bookmark