# Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection

arXiv: https://arxiv.org/abs/2003.12338 · HTML: https://mlsanity.com/paper/2003.12338/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 28 of 28)

- [Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead](https://mlsanity.com/paper/2311.02782/) (2023)
- [Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification](https://mlsanity.com/paper/2104.07235/) (2021)
- [Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans](https://mlsanity.com/paper/2101.05442/) (2021)
- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus](https://mlsanity.com/paper/2103.07055/) (2021)
- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2408.03888/) (2024)
- [CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis](https://mlsanity.com/paper/2106.10230/) (2021)
- [Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation](https://mlsanity.com/paper/2112.02478/) (2021)
- [CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environments](https://mlsanity.com/paper/2510.26006/) (2025)
- [Semi-supervised Learning for COVID-19 Image Classification via ResNet](https://mlsanity.com/paper/2103.06140/) (2021)
- [Learning Invariant Representations across Domains and Tasks](https://mlsanity.com/paper/2103.05114/) (2021)
- [COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images](https://mlsanity.com/paper/2104.07279/) (2021)
- [Evolving Deep Convolutional Neural Network by Hybrid Sine-Cosine and Extreme Learning Machine for Real-time COVID19 Diagnosis from X-Ray Images](https://mlsanity.com/paper/2105.14192/) (2021)
- [Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring](https://mlsanity.com/paper/2105.15094/) (2021)
- [CNN-based Local Vision Transformer for COVID-19 Diagnosis](https://mlsanity.com/paper/2207.02027/) (2022)
- [AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection](https://mlsanity.com/paper/2405.03075/) (2024)
- [Reducing Risk and Uncertainty of Deep Neural Networks on Diagnosing COVID-19 Infection](https://mlsanity.com/paper/2104.14029/) (2021)
- [AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images](https://mlsanity.com/paper/2105.10239/) (2021)
- [WisdomNet: Prognosis of COVID-19 with Slender Prospect of False Negative Cases and Vaticinating the Probability of Maturation to ARDS using Posteroanterior Chest X-Rays](https://mlsanity.com/paper/2107.01392/) (2021)
- [Understanding Human Innate Immune System Dependencies using Graph Neural Networks](https://mlsanity.com/paper/2108.02872/) (2021)
- [Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNN](https://mlsanity.com/paper/2109.02103/) (2021)
- [Interpretation of Chest x-rays affected by bullets using deep transfer learning](https://mlsanity.com/paper/2203.13461/) (2022)
- [COVID-19 Severity Classification on Chest X-ray Images](https://mlsanity.com/paper/2205.12705/) (2022)
- [Bridging Machine Learning and Sciences: Opportunities and Challenges](https://mlsanity.com/paper/2210.13441/) (2022)
- [Detection of COVID19 in Chest X-Ray Images Using Transfer Learning](https://mlsanity.com/paper/2304.04161/) (2023)
- [How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2312.03804/) (2023)
- [Training Better Deep Learning Models Using Human Saliency](https://mlsanity.com/paper/2410.16190/) (2024)

## Abstract (arXiv)

Cluster of viral pneumonia occurrences during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention, particularly when other chest imaging modalities are less available. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images. The evolution of viruses and the emergence of novel mutated viruses further result in substantial dataset shift, which greatly limits the performance of classification approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough or the confidence score estimated by the confidence prediction module is small enough, we accept the input as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to reinforce the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 18,619 non-viral pneumonia cases, and 18,774 healthy controls.