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Article citations


Pisano, E.D., Cole, E.B., Kistner, E.O., Muller, K.E., Hemminger, B.M., Brown, M. L., Johnston, R.E., Kuzmiak, C.M., Braeuning, P., Freimanis, R.I., Soo, M.S., Baker, J.A. and Walsh, R. (2002) Interpretation of Digital Mammograms: Comparison of Speed and Accuracy of Soft-Copy versus Printed-Film Display. Radiology, 223, 483-488.

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.