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

TL;DR

This paper introduces a confidence-aware anomaly detection model for rapid viral pneumonia screening on chest X-ray images, addressing dataset shift and diversity in visual appearances, and demonstrating superior performance over traditional classification methods.

Contribution

The paper proposes a novel one-class classification-based anomaly detection approach with confidence estimation for viral pneumonia detection on X-ray images, improving robustness to dataset shifts.

Findings

Outperforms binary classification models on clinical dataset

Effectively detects diverse viral pneumonia cases

Handles dataset shift with confidence-aware modeling

Abstract

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.