COVID-19 Radiological Lung Imaging
portes grátis
COVID-19 Radiological Lung Imaging
A Classic Artificial Intelligence Framework
Suri, Jasjit; Saba, Luca; Agarwal, Sushant
Elsevier Science Publishing Co Inc
11/2025
450
Mole
Inglês
9780443138744
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Section 1: X-Ray Lung Imaging using Deep Learning
1. Lung Segmentation using Lung X-ray Scans: U-Series
2. Lung Classification using Lung X-ray Scans
3. Heatmap using Explainable AI on Lung X-ray Scans
4. Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
5. Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
6. Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
7. Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
8. Hybrid Deep Learning in a Multicenter Framework for Automated COVID-19 Lung Segmentation
Section 3: Pruning & Optimization Deep Learning Techniques for Computed Tomography COVID-19 Imaging
9. Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
10. Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
11. Pruning of COVID-19 Computed Tomography based Lung Segmentation Deep Learning Models for Storage and Performance Improvement and its Validation using Class Activation Map Techniques
Section 4: Deep Learning on Edge Devices for COVID-19 & Bias Measurements in Deep Learning
12. Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
13. Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
14. Five Strategies for Bias Estimation in Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients
Section 5: Deep Learning on Cloud for COVID-19 and Explainable AI for Validation
15. Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
16. Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans in a Cloud Environment
Section 6: Medical Impact and AI Application for COVID-19 in Lung Pathologies
17. Lung COVID from pathology to radiological features
18. Lung COVID and pulmonary embolism
19. Classification systems in X-ray for Lung pathology COVID based
20. Classification systems in CT for Lung pathology COVID based
21. A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.
1. Lung Segmentation using Lung X-ray Scans: U-Series
2. Lung Classification using Lung X-ray Scans
3. Heatmap using Explainable AI on Lung X-ray Scans
4. Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
5. Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
6. Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
7. Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
8. Hybrid Deep Learning in a Multicenter Framework for Automated COVID-19 Lung Segmentation
Section 3: Pruning & Optimization Deep Learning Techniques for Computed Tomography COVID-19 Imaging
9. Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
10. Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
11. Pruning of COVID-19 Computed Tomography based Lung Segmentation Deep Learning Models for Storage and Performance Improvement and its Validation using Class Activation Map Techniques
Section 4: Deep Learning on Edge Devices for COVID-19 & Bias Measurements in Deep Learning
12. Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
13. Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
14. Five Strategies for Bias Estimation in Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients
Section 5: Deep Learning on Cloud for COVID-19 and Explainable AI for Validation
15. Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
16. Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans in a Cloud Environment
Section 6: Medical Impact and AI Application for COVID-19 in Lung Pathologies
17. Lung COVID from pathology to radiological features
18. Lung COVID and pulmonary embolism
19. Classification systems in X-ray for Lung pathology COVID based
20. Classification systems in CT for Lung pathology COVID based
21. A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.
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Artificial Intelligence; Deep Learning; Classification; Segmentation; Optimization; Pruning; Bias, Cloud-Deployment; Edge Devices; and Computer-Aided Diagnosis
Section 1: X-Ray Lung Imaging using Deep Learning
1. Lung Segmentation using Lung X-ray Scans: U-Series
2. Lung Classification using Lung X-ray Scans
3. Heatmap using Explainable AI on Lung X-ray Scans
4. Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
5. Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
6. Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
7. Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
8. Hybrid Deep Learning in a Multicenter Framework for Automated COVID-19 Lung Segmentation
Section 3: Pruning & Optimization Deep Learning Techniques for Computed Tomography COVID-19 Imaging
9. Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
10. Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
11. Pruning of COVID-19 Computed Tomography based Lung Segmentation Deep Learning Models for Storage and Performance Improvement and its Validation using Class Activation Map Techniques
Section 4: Deep Learning on Edge Devices for COVID-19 & Bias Measurements in Deep Learning
12. Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
13. Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
14. Five Strategies for Bias Estimation in Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients
Section 5: Deep Learning on Cloud for COVID-19 and Explainable AI for Validation
15. Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
16. Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans in a Cloud Environment
Section 6: Medical Impact and AI Application for COVID-19 in Lung Pathologies
17. Lung COVID from pathology to radiological features
18. Lung COVID and pulmonary embolism
19. Classification systems in X-ray for Lung pathology COVID based
20. Classification systems in CT for Lung pathology COVID based
21. A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.
1. Lung Segmentation using Lung X-ray Scans: U-Series
2. Lung Classification using Lung X-ray Scans
3. Heatmap using Explainable AI on Lung X-ray Scans
4. Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
5. Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
6. Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
7. Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
8. Hybrid Deep Learning in a Multicenter Framework for Automated COVID-19 Lung Segmentation
Section 3: Pruning & Optimization Deep Learning Techniques for Computed Tomography COVID-19 Imaging
9. Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
10. Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
11. Pruning of COVID-19 Computed Tomography based Lung Segmentation Deep Learning Models for Storage and Performance Improvement and its Validation using Class Activation Map Techniques
Section 4: Deep Learning on Edge Devices for COVID-19 & Bias Measurements in Deep Learning
12. Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
13. Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
14. Five Strategies for Bias Estimation in Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients
Section 5: Deep Learning on Cloud for COVID-19 and Explainable AI for Validation
15. Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
16. Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans in a Cloud Environment
Section 6: Medical Impact and AI Application for COVID-19 in Lung Pathologies
17. Lung COVID from pathology to radiological features
18. Lung COVID and pulmonary embolism
19. Classification systems in X-ray for Lung pathology COVID based
20. Classification systems in CT for Lung pathology COVID based
21. A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
