ArticlePattern recognition2021
Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 109 citations in OpenAlex.
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Diagnosing acute bilirubin encephalopathy in neonates using MRI-based deep learning model.BMC pediatrics · 2025Article
- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- Multimodal graph attention network for COVID-19 outcome prediction.Scientific reports · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- Automated semantic lung segmentation in chest CT images using deep neural network.Neural computing & applications · 2023Article
- Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients.Medical & biological engineering & computing · 2022Article
- COVID-19 contact tracking by group activity trajectory recovery over camera networks.Pattern recognition · 2022Article
- COVID-19 Infection Segmentation and Severity Assessment Using a Self-Supervised Learning Approach.Diagnostics (Basel, Switzerland) · 2022Article
- Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.Computer methods and programs in biomedicine · 2022Article
- Contour-enhanced attention CNN for CT-based COVID-19 segmentation.Pattern recognition · 2022Article
- CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.Applied soft computing · 2022Article
- Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images.Pattern recognition · 2022Article
- AI-Based human audio processing for COVID-19: A comprehensive overview.Pattern recognition · 2022Article
- Efficient COVID-19 testing via contextual model based compressive sensing.Pattern recognition · 2022Article
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
- One Shot Model For The Prediction of COVID-19 And Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features.Applied soft computing · 2022Article
- Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M
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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.