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BI-RADS density categorization using deep neural networks
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Paper Abstract

The Breast Imaging and Reporting Data System (BI-RADS) density score is a qualitative measure and thus subject to inter- and intra-radiologist variability. In this study we investigated the possibility of fine-tuning a state-of-the-art deep neural networks for (i) distinguishing fatty breasts (BI-RADS I and II) from dense ones (BI-RADS III and IV), (ii) classifying the low risk group into BIRADS I and II, and (iii) classifying the high risk group into BIRADS III and IV. To do so 3813 images acquired from nine mammography units and three manufacturers were used to train an Inception- V3 network architecture. The network was pre-trained on the ImageNet data set and we trained it on our dataset using transfer learning. Before feeding the images into the input layer of Inception- V3, the breast tissue was segmented from the background and the pectoral muscle was excluded from the image in the mediolateral oblique view. Images were then cropped by using the breast bounding box and resized to make the images compatible with the input layer of the network. The performance of the network was evaluated on a blinded test set of 150 mammograms acquired from 14 mammography units provided by six manufacturers. The reference density value for these images was obtained based on the consensus of three radiologists. The network achieved an accuracy of 92.0% in high versus low risk classification. For the second and third classification tasks, the overall accuracy was 85.9% and 86.1%. When results from all three classifications combined, the networks achieved an accuracy of 83.33% and a Cohen’s kappa of 0.775 (95% CI: 0.694-0.856) for four-point density categorization. The obtained results suggest that a deep learning-based computerized tool can be used for providing BI-RADS density scores.

Paper Details

Date Published: 4 March 2019
PDF: 7 pages
Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520N (4 March 2019); doi: 10.1117/12.2513185
Show Author Affiliations
Ziba Gandomkar, The Univ. of Sydney (Australia)
Moayyad E. Suleiman, The Univ. of Sydney (Australia)
Delgermaa Demchig, The Univ. of Sydney (Australia)
Patrick C. Brennan, The Univ. of Sydney (Australia)
Mark F. McEntee, The Univ. of Sydney (Australia)


Published in SPIE Proceedings Vol. 10952:
Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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