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Article citations


Duffy, S.W., Tabár, L., Chen, H.H., Holmqvist, M., Yen, M.F., Abdsalah, S., Epstein, B., Frodis, E., Ljungberg, E., Hedborg-Melander, C., Sundbom, A., Tholin, M., Wiege, M., Åkerlund, A., Wu, H., Tung, T., Chiu, Y., Chiu, C., Huang, C., Smith, R. A., Rosen, M., Stenbuck, M. and Holmberg, L. (2002) The Impact of Organized Mammography Service Screening on Breast Carcinoma Mortality in Seven Swedish Counties. Cancer, 95, 458-469.

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.