Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image

TL;DR

This paper introduces a calibrated bagging deep learning ensemble for COVID-19 chest X-ray image segmentation, improving accuracy and reducing uncertainty in predictions for safer medical diagnosis.

Contribution

It proposes a novel ensemble method combining bagging and calibration to enhance segmentation accuracy and uncertainty estimation in COVID-19 CXR analysis.

Findings

Improved segmentation performance on large CXR dataset

Reduced prediction uncertainty in deep learning models

Validated effectiveness through extensive experiments

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainties.