Breast Density Quantification Using Weakly Annotated Dataset
Mickael Tardy, Bruno Scheffer, Diana Mateus · 2019
Breast density is known to be an efficient biomarker for cancer risk, and of particular interest in early breast cancer detection, when masses are not yet visible. The quantification of the breast density is difficult due to limitations of mammography imaging, as well as to the ambiguities in defining the limits of the relevant regions. Though inherently a regression task, breast density quantification has been typically approached as a rough classification problem. In this paper, we model the problem of breast density evaluation as an image-wise regression task that seeks to quantify the percentage of fibroglandular tissue. We propose a deep learning method offering a clinically acceptable estimate with low requirements on expert annotations. We also discuss the use of the X-ray acquisition parameters as additional input to the neural network. Our best performing model yields an optimistic mean absolute error around 6.0% of breast density.