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 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.
The raw data is at https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data
If you're running the pipeline, please cite:
Han Y, Chen C, Tewfik AH, Ding Y, Peng Y. Pneumonia Detection on Chest X-ray using Radiomic
Features and Contrastive Learning. In IEEE International Symposium on Biomedical Imaging (ISBI).
2021;247-251. https://doi.org/10.1109/ISBI48211.2021.9433853
This project was supported by National Library of Medicineunder award number 4R00LM013001 and Amazon MachineLearning Grant.