ArticleMedical image analysis2021
Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 2 of them syntheses that pooled it.
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Who cites it
71 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2026Article
- Multiscale segmentation using hierarchical phase-contrast tomography and deep learning.PLoS computational biology · 2026Article
- Artificial intelligence and precision nursing in the operating room: transforming perioperative safety and surgical outcomes.Frontiers in bioengineering and biotechnology · 2026Review
- Multimodal text guided network for chest CT pneumonia classification.Scientific reports · 2025Article
- Removing Adversarial Noise in X-ray Images via Total Variation Minimization and Patch-Based Regularization for Robust Deep Learning-based Diagnosis.Journal of imaging informatics in medicine · 2024Article
- The Integration of Radiomics and Artificial Intelligence in Modern Medicine.Life (Basel, Switzerland) · 2024Review
- Semi-supervised CT image segmentation via contrastive learning based on entropy constraints.Biomedical engineering letters · 2024Article
- Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT.Tomography (Ann Arbor, Mich.) · 2024Article
- Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network.Diagnostics (Basel, Switzerland) · 2024Article
- Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review.JMIR medical informatics · 2024Review
- Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images.Journal of imaging informatics in medicine · 2024Article
- COVID-19 infection analysis framework using novel boosted CNNs and radiological images.Scientific reports · 2023Article
- Multimodal graph attention network for COVID-19 outcome prediction.Scientific reports · 2023Article
- COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs.Expert systems with applications · 2023Article
- Artificial Intelligence and Infectious Disease Imaging.The Journal of infectious diseases · 2023Article
- Deep-Learning-Based Whole-Lung and Lung-Lesion Quantification Despite Inconsistent Ground Truth: Application to Computerized Tomography in SARS-CoV-2 Nonhuman Primate Models.Academic radiology · 2023Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
11 more citing papers are in PubMed but not listed here.
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12 authors.
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Abstract
The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.
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