  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.
