Oeffinger, K.C., Fontham, E.T., Etzioni, R., Herzig, A., Michaelson, J.S., Shih, Y. C.T., Walter, L.C., Church, T.R., Flowers, C.R., LaMonte, S.J., Wolf, A.M., DeSantis, C., Lortet-Tieulent, J., Andrews, K., Manassaram-Baptiste, D., Saslow, D., Smith, R.A., Braeley, O.W., Wender, R. (2015) Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update from the American Cancer Society. JAMA, 314, 1599-1614.
https://doi.org/10.1001/jama.2015.12783

has been cited by the following article:

• TITLE: Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network

  AUTHORS: Kensuke Umehara, Junko Ota, Takayuki Ishida

  KEYWORDS: Super-Resolution, Deep-Learning, Artificial Intelligence, Breast Imaging Reporting and Data System (BI-RADS), Mammography

  JOURNAL NAME: Open Journal of Medical Imaging, Vol.7 No.4, November 13, 2017

  ABSTRACT: Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p p Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.