Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability

Kaiser, N, Fieselmann, A, Vesal, S et al. (4 more authors) (2019) Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability. In: Nishikawa, RM and Samuelson, FW, (eds.) Proceedings of SPIE: Progress in Biomedical Optics and Imaging. SPIE Medical Imaging 2019, 16-21 Feb 2019, San Diego, CA, United States. SPIE . ISBN 9781510625518

Abstract

Metadata

Authors/Creators:
  • Kaiser, N
  • Fieselmann, A
  • Vesal, S
  • Ravikumar, N
  • Ritschl, L
  • Kappler, S
  • Maier, A
Copyright, Publisher and Additional Information: © 2019, Society of Photo-Optical Instrumentation Engineers (SPIE). This is an author produced version of a paper published in Proceedings of SPIE: Progress in Biomedical Optics and Imaging. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Breast; Breast cancer; Convolutional neural networks; Image segmentation; Neural networks; Tissues; Feature extraction
Dates:
  • Accepted: 15 October 2018
  • Published (online): 4 March 2019
  • Published: 14 June 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 05 Aug 2019 11:14
Last Modified: 17 Sep 2019 10:31
Status: Published
Publisher: SPIE
Identification Number: https://doi.org/10.1117/12.2513420

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