Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning

TL;DR

This paper introduces a novel framework combining radiomic features and contrastive learning to improve pneumonia detection in chest X-rays, achieving higher accuracy and better interpretability compared to existing models.

Contribution

It presents a new approach that integrates radiomics and contrastive learning for pneumonia detection, enhancing performance and interpretability over prior deep learning methods.

Findings

Achieves >10% higher F1-score than state-of-the-art models.

Improves model interpretability in pneumonia detection.

Demonstrates effectiveness on RSNA dataset.

Abstract

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. With the rise of deep learning, the explainability of deep neural networks on chest X-ray diagnosis remains opaque. In this study, we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models (> 10% in F1-score) and increases the model's interpretability.