Visual interpretability of deep learning algorithms in medical applications

Abstract

Artificial intelligence is having a very big boost in recent times, and after the success of deep learning algorithms in many applications, they are also providing successful results for medical imaging, especially because of the good performance of convolutional neural networks. However, the black box behaviour of these networks makes it very difficult to assign them tasks that an expert human normally does. This project aims to interpret in human terms what a convolutional neural network trained to classify fetal different ultrasound planes is based on. We use transfer learning to build a network with good performance in the classification task and apply interpretability techniques on it. These methods include Activation Maximization, Saliency Maps, Occlusion Sensitivity Maps, Class Activation Mapping and LIME. The trained network is able to classify fetal ultrasound images with an accuracy of 91.7%, and we provide a robust interpretation of its performance that allows us to understand the most important characteristics of each class for the model.