Feature learning using Stacked Autoencoder for Multimodal Fusion, Shared and Cross Learning on Medical Images

The analysis of medical images and to find meaningful patterns in it is a cumbersome task, even with the use of techniques of Computer Vision when the dataset is very large. In such a situation deep learning is a handy tool, because of its ability to learn and extract meaningful patterns and features from the images. The use of multiple modalities of training data to train system has been in practice for conventional machine learning algorithms. Here, in this paper, we are going to present a Deep Learning based architecture for extraction of features from large training set of medical images. The deep learning model is tested against conventional techniques by performing Multimodal fusion, Shared Learning and Cross Learning on it. It was found out that Deep Learning model performs superior than the conventional techniques in multimodal fusion and shared learning settings.