Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation

TL;DR

This paper introduces a novel domain adaptation method for COVID-19 severity assessment from CT images that addresses class imbalance, domain discrepancy, and heterogeneous features to improve cross-site classification accuracy.

Contribution

It proposes a two-component domain adaptation framework combining class-balanced boosting sampling and representation learning with prototype triplet loss, MMD loss, and multi-view reconstruction.

Findings

Outperforms recent domain adaptation methods on cross-site COVID-19 severity assessment

Effectively handles class imbalance and domain discrepancies

Improves generalization across different data collection sites.

Abstract

Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches.