ArticleIEEE transactions on medical imaging2022
Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Monitoring pneumonia onset and severe respiratory infectious diseases using quantitative computed tomography, validation amidst the coronavirus disease 2019 outbreak at the end of 2022 in the Chinese mainland.Journal of thoracic disease · 2026Article
- BrainGraphNet-ViT model: a hybrid model combining vision transformers and graph convolutional networks for brain tumor diagnosis.Scientific reports · 2026Article
- The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Attentive continuous generative self-training for unsupervised domain adaptive medical image translation.Medical image analysis · 2023Article
- Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation.ArXiv · 2023Article
- COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19.European radiology · 2023Article
- iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction.Nature protocols · 2023Review
- Automatic Classification of Histopathology Images across Multiple Cancers Based on Heterogeneous Transfer Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information.Infection and drug resistance · 2023Article
- Convolutional neural network based CT scan classification method for COVID-19 test validation.Smart health (Amsterdam, Netherlands) · 2022Article
- Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software.Journal of personalized medicine · 2021Article
- Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment.Journal of personalized medicine · 2021Review
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12 authors.
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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.
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