ArticleApplied intelligence (Dordrecht, Netherlands)2022
Unsupervised domain adaptation based COVID-19 CT infection segmentation network.
Article in Applied intelligence (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and convolutional neural networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- The Advent of Domain Adaptation into Artificial Intelligence for Gastrointestinal Endoscopy and Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images.Biomedical signal processing and control · 2023Article
- Association between emphysema and other pulmonary computed tomography patterns in COVID-19 pneumonia.Journal of medical virology · 2023Article
- Lesion segmentation in lung CT scans using unsupervised adversarial learning.Medical & biological engineering & computing · 2022Article
- COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.Computers in biology and medicine · 2022Article
- A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases.Biomedical signal processing and control · 2022Article
- Deep feature fusion classification network (DFFCNet): Towards accurate diagnosis of COVID-19 using chest X-rays images.Biomedical signal processing and control · 2022Article
- A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach.Applied soft computing · 2022Article
- An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation.Applied soft computing · 2021Article
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Authors and funding
4 authors.
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Abstract
Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnostic approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accurate segmentation remains a major challenge. In this paper, we propose an unsupervised domain adaptation based segmentation network to improve the segmentation performance of the infection areas in COVID-19 CT images. In particular, we propose to utilize the synthetic data and limited unlabeled real COVID-19 CT images to jointly train the segmentation network. Furthermore, we develop a novel domain adaptation module, which is used to align the two domains and effectively improve the segmentation network's generalization capability to the real domain. Besides, we propose an unsupervised adversarial training scheme, which encourages the segmentation network to learn the domain-invariant feature, so that the robust feature can be used for segmentation. Experimental results demonstrate that our method can achieve state-of-the-art segmentation performance on COVID-19 CT images.
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