Image Embedding and Model Ensembling for Automated Chest X-Ray Interpretation

Edoardo Giacomello, Pier Luca Lanzi, Daniele Loiacono, Luca Nassano

Abstract: Chest X-ray (CXR) is perhaps the most frequently-performed radiological investigation globally. In this work, we present and study several machine learning approaches to develop automated CXR diagnostic models. In particular, we trained several Convolutional Neural Networks (CNN) on the CheXpert dataset, a large collection of more than 200k CXR labeled images. Then, we used the trained CNNs to compute embeddings of the CXR images, in order to train two sets of tree-based classifiers from them. Finally, wed escribed and compared three ensembling strategies to combine together the classifiers trained. Rather than expecting some performance-wise benefits, o ur goal i n this work iss howing that t he above two methodologies, i.e., the extraction of image embeddings and models ensembling, can be effective and viable to solve tasks that require medical imaging understanding. Our results in that perspective are encouraging and worthy of further investigation.