Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning
This addresses the problem of automating pneumonia diagnosis to reduce radiologist burnout and delays, though it appears incremental as it builds on existing radiomics and contrastive learning methods.
The paper tackled pneumonia detection in chest X-rays by proposing a framework combining radiomic features and contrastive learning, achieving over 10% improvement in F1-score compared to state-of-the-art models on the RSNA dataset.
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.