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
Mammographic breast density is an important risk marker in breast cancer screening. The ACR BI-RADS guidelines (5th ed.) define four breast density categories that can be dichotomized by the two super-classes dense" and not dense". Due to the qualitative description of the categories, density assessment by radiologists is characterized by a high inter-observer variability. To quantify this variability, we compute the overall percentage agreement (OPA) and Cohen's kappa of 32 radiologists to the panel majority vote based on the two super-classes. Further, we analyze the OPA between individual radiologists and compare the performances to an automated assessment via a convolutional neural network (CNN). The data used for evaluation contains 600 breast cancer screening examinations with four views each. The CNN was designed to take all views of an examination as input and trained on a dataset with 7186 cases to output one of the two super-classes. The highest agreement to the panel majority vote (PMV) achieved by a single radiologist is 99%, the lowest score is 71% with a mean of 89%. The OPA of two individual radiologists ranges from a maximum of 97.5% to a minimum of 50.5% with a mean of 83%. Cohen's kappa values of radiologists to the PMV range from 0.97 to 0.47 with a mean of 0.77. The presented algorithm reaches an OPA to all 32 radiologists of 88% and a kappa of 0.75. Our results show that inter-observer variability for breast density assessment is high even if the problem is reduced to two categories and that our convolutional neural network can provide labelling comparable to an average radiologist. We also discuss how to deal with automated classification methods for subjective tasks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Keywords: | Breast; Breast cancer; Convolutional neural networks; Image segmentation; Neural networks; Tissues; Feature extraction |
Dates: |
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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: | 29 Nov 2024 10:27 |
Status: | Published |
Publisher: | SPIE |
Identification Number: | 10.1117/12.2513420 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149273 |