Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features

TL;DR

This study explores using beta-Variational Autoencoders to extract features from chest X-ray images, enabling classification through machine learning models, demonstrating a promising approach for practical medical image analysis.

Contribution

The paper introduces a novel method combining beta-VAE feature extraction with ensemble machine learning for CXR classification, emphasizing generality and real-world applicability.

Findings

Encouraging classification results using VAE-derived features

Ensemble models improve accuracy without additional training

Viability of autoencoder high-level features for medical image classification

Abstract

Chest X-Ray (CXR) is one of the most common diagnostic techniques used in everyday clinical practice all around the world. We hereby present a work which intends to investigate and analyse the use of Deep Learning (DL) techniques to extract information from such images and allow to classify them, trying to keep our methodology as general as possible and possibly also usable in a real world scenario without much effort, in the future. To move in this direction, we trained several beta-Variational Autoencoder (beta-VAE) models on the CheXpert dataset, one of the largest publicly available collection of labeled CXR images; from these models, latent features have been extracted and used to train other Machine Learning models, able to classify the original images from the features extracted by the beta-VAE. Lastly, tree-based models have been combined together in ensemblings to improve the results without the necessity of further training or models engineering. Expecting some drop in pure performance with the respect to state of the art classification specific models, we obtained encouraging results, which show the viability of our approach and the usability of the high level features extracted by the autoencoders for classification tasks.