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22 May 2020 Automatic density prediction in low dose mammography
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Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 115131D (2020)
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium
Estimation of breast density for cancer risk prediction is generally achieved by analysis of full-field digital mammograms. Conventional digital mammography should be avoided if possible in young women because of concerns about potential cancer induction, particularly in those with dense breasts who receive higher doses. This precludes repeated examinations over a short timescale to assess density change. We assess whether density can be accurately estimated in low dose mammograms with one-tenth of the standard dose, with the aim of providing a safe and effective method for use in younger women which is suitable for serial density measurement. We present analysis of data from an on-going clinical trial in which both standard and low dose mammograms are acquired under the same compression. We used both an existing convolutional neural network model designed to estimate breast density and a new model developed using a transfer learning approach. We then applied three methods to estimate density on the low dose mammograms: training on a different mammogram dataset; using simulated low dose data; and training directly on low dose mammograms using cross-validation. Pearson correlation coefficients between measurements on full dose and low dose mammograms ranged from 0.92 to 0.98 with the root mean squared error ranging between 3.37 and 7.27. Our results indicate that accurate density measurements can be made using low dose mammograms.
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Steven Squires, Georgia Ionescu, Elaine F. Harkness, Alistair Mackenzie, D. Gareth Evans, Anthony Maxwell, Sacha Howell, and Susan M. Astley "Automatic density prediction in low dose mammography", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115131D (22 May 2020);

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