ArticleRadiology. Cardiothoracic imaging2020
Longitudinal Assessment of COVID-19 Using a Deep Learning-based Quantitative CT Pipeline: Illustration of Two Cases.
Article in Radiology. Cardiothoracic imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed.
- Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model.Journal of family medicine and primary care · 2024Article
- Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information.Quantitative imaging in medicine and surgery · 2024Article
- Fully feature fusion based neural network for COVID-19 lesion segmentation in CT images.Biomedical signal processing and control · 2023Article
- A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images.Biomedical signal processing and control · 2023Article
- Preliminary Stages for COVID-19 Detection Using Image Processing.Diagnostics (Basel, Switzerland) · 2022Review
- MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images.Signal processing. Image communication · 2022Article
- Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review.Neural computing & applications · 2022Review
- Evolving operational guidance and experiences for radiology and nuclear medicine facilities in response to and beyond the COVID-19 pandemic.The British journal of radiology · 2022Review
- Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN.Methods (San Diego, Calif.) · 2022Article
- Effective multiscale deep learning model for COVID19 segmentation tasks: A further step towards helping radiologist.Neurocomputing · 2022Article
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
- Virtual Reality visualization for computerized COVID-19 lesion segmentation and interpretation.Biomedical signal processing and control · 2022Article
- Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image.PloS one · 2022Article
- Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research.Artificial intelligence review · 2022Article
- Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey.Contrast media & molecular imaging · 2022Review
- A deep adversarial model for segmentation-assisted COVID-19 diagnosis using CT images.EURASIP journal on advances in signal processing · 2022Article
- Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study.Journal of healthcare engineering · 2022Review
- A transfer learning based deep learning model to diagnose covid-19 CT scan images.Health and technology · 2022Article
- Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation.IEEE journal of biomedical and health informatics · 2021Article
- Label-Free Segmentation of COVID-19 Lesions in Lung CT.IEEE transactions on medical imaging · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.