A deep convolutional neural network for breast density assessment: Optimization and explainability.

This work aims to develop a method for deep neural network explainability. It is the ability to explain the algorithm behaviour and its predictions when it has a deep multi-layer nonlinear structure. This is a critical issue in Artificial Intelligence. An already developed deep Residual Convolutional Neural Network is able to automatically classify mammograms into breast density classes. The explainability of the network has been studied through various analyses and visualization techniques, assessing trust in the model, which is fundamental for its potential application in clinical practice, and also achieving a performance improvement in terms of accuracy.