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Breast density estimation is one of the key tasks in recognizing individualspredisposed to breast cancer. It is often challenging because of low contrastand fluctuations in mammograms' fatty tissue background. Most of the time, thebreast density is estimated manually where a radiologist assigns one of thefour density categories decided by the Breast Imaging and Reporting DataSystems (BI-RADS). There have been efforts in the direction of automating abreast density classification pipeline.  Breast density estimation is one of the key tasks performed during ascreening exam. Dense breasts are more susceptible to breast cancer. Thedensity estimation is challenging because of low contrast and fluctuations inmammograms' fatty tissue background. Traditional mammograms are being replacedby tomosynthesis and its other low radiation dose variants (for exampleHologic' Intelligent 2D and C-View). Because of the low-dose requirement,increasingly more screening centers are favoring the Intelligent 2D view andC-View. Deep-learning studies for breast density estimation use only a singlemodality for training a neural network. However, doing so restricts the numberof images in the dataset. In this paper, we show that a neural network trainedon all the modalities at once performs better than a neural network trained onany single modality. We discuss these results using the area under the receiveroperator characteristics curves.

A multi-reconstruction study of breast density estimation using Deep Learning