ArticleMedical image analysis2021
COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework.
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 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline 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
- Detection-guided deep learning-based model with spatial regularization for lung nodule segmentation.Quantitative imaging in medicine and surgery · 2025Article
- Comparing Different Data Partitioning Strategies for Segmenting Areas Affected by COVID-19 in CT Scans.Diagnostics (Basel, Switzerland) · 2024Article
- Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT.Tomography (Ann Arbor, Mich.) · 2024Article
- CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation.Biomedical engineering letters · 2024Article
- Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.Journal of imaging · 2023Article
- COVID-19 infection analysis framework using novel boosted CNNs and radiological images.Scientific reports · 2023Article
- Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging.BMC bioinformatics · 2023Article
- Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.Sensors (Basel, Switzerland) · 2023Article
- MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.Information sciences · 2023Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- Interpretable Differential Diagnosis of Non-COVID Viral Pneumonia, Lung Opacity and COVID-19 Using Tuned Transfer Learning and Explainable AI.Healthcare (Basel, Switzerland) · 2023Article
- COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.International journal of imaging systems and technology · 2023Article
- COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture.PloS one · 2023Article
- DeepPDT-Net: predicting the outcome of photodynamic therapy for chronic central serous chorioretinopathy using two-stage multimodal transfer learning.Scientific reports · 2022Article
- Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning.Computers in biology and medicine · 2022Article
- Deep feature fusion classification network (DFFCNet): Towards accurate diagnosis of COVID-19 using chest X-rays images.Biomedical signal processing and control · 2022Article
- COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.Scientific reports · 2022Article
- Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model.Journal of healthcare engineering · 2022Article
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7 authors.
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Abstract
With the global outbreak of COVID-19 in early 2020, rapid diagnosis of COVID-19 has become the urgent need to control the spread of the epidemic. In clinical settings, lung infection segmentation from computed tomography (CT) images can provide vital information for the quantification and diagnosis of COVID-19. However, accurate infection segmentation is a challenging task due to (i) the low boundary contrast between infections and the surroundings, (ii) large variations of infection regions, and, most importantly, (iii) the shortage of large-scale annotated data. To address these issues, we propose a novel two-stage cross-domain transfer learning framework for the accurate segmentation of COVID-19 lung infections from CT images. Our framework consists of two major technical innovations, including an effective infection segmentation deep learning model, called nCoVSegNet, and a novel two-stage transfer learning strategy. Specifically, our nCoVSegNet conducts effective infection segmentation by taking advantage of attention-aware feature fusion and large receptive fields, aiming to resolve the issues related to low boundary contrast and large infection variations. To alleviate the shortage of the data, the nCoVSegNet is pre-trained using a two-stage cross-domain transfer learning strategy, which makes full use of the knowledge from natural images (i.e., ImageNet) and medical images (i.e., LIDC-IDRI) to boost the final training on CT images with COVID-19 infections. Extensive experiments demonstrate that our framework achieves superior segmentation accuracy and outperforms the cutting-edge models, both quantitatively and qualitatively.
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