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

arXiv: https://arxiv.org/abs/2101.04269 · HTML: https://mlsanity.com/paper/2101.04269/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays](https://mlsanity.com/paper/1908.07926/) (2019)

## Built on by (most cited first, 11 of 11)

- [Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning](https://mlsanity.com/paper/2210.06044/) (2022)
- [Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays](https://mlsanity.com/paper/2207.04394/) (2022)
- [Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop](https://mlsanity.com/paper/2104.04968/) (2021)
- [RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models](https://mlsanity.com/paper/2410.00307/) (2024)
- [Unsupervised Local Discrimination for Medical Images](https://mlsanity.com/paper/2108.09440/) (2021)
- [DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification](https://mlsanity.com/paper/2308.00475/) (2023)
- [A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images](https://mlsanity.com/paper/2309.02555/) (2023)
- [A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI](https://mlsanity.com/paper/2211.12565/) (2022)
- [Enhancing COVID-19 Diagnosis through Vision Transformer-Based Analysis of Chest X-ray Images](https://mlsanity.com/paper/2306.06914/) (2023)
- [AGA: An adaptive group alignment framework for structured medical cross-modal representation learning](https://mlsanity.com/paper/2507.23402/) (2025)
- [SparseContrast: Dynamic Sparse Attention for Efficient and Accurate Contrastive Learning in Medical Imaging](https://mlsanity.com/paper/2605.00887/) (2026)

## Abstract (arXiv)

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.