Beyond Breast Density Classification Future Perspectives
We present a work on breast density classification performed with deep residual neural network and we discuss the future perspectives on this kind of study. Breast density is one of the most important breast cancer risk factor. However, it is not included in any risk model because of its variability among women and correlation with other breast cancer risk factors. We trained a deep CNN to perform breast density classification in the four class BI RADS standard. We obtained very good results compared to our literature knowledge in terms of accuracy. To improve the robustness of our algorithm, first, we are collecting exams of not only healthy women but also of symptomatic one. Second, we are including in the dataset some certain interval cancer to perform a classification. Third, we are studying how to quantitatively measure the precision of the network in capturing the significative part of the images.