COVID-19 Radiological Lung Imaging
portes grátis
COVID-19 Radiological Lung Imaging
A Classic Artificial Intelligence Framework
Saba, Luca; Agarwal, Sushant; Suri, Jasjit
Elsevier Science Publishing Co Inc
07/2024
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
CH 1: Lung Segmentation using Lung X-ray Scans: U-Series
CH 2: Lung Classification using Lung X-ray Scans
CH 3: Heatmap using Explainable AI on Lung X-ray Scans
CH 4: Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
CH 5: Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
CH 6: Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
CH 7: Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
CH 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
CH 9: Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
CH 10: Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
CH 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
CH 12: Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
CH 13: Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
CH 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
CH 15: Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
CH 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
CH 17: Lung COVID from pathology to radiological features
CH 18: Lung COVID and pulmonary embolism
CH 19: Classification systems in X-ray for Lung pathology COVID based
CH 20: Classification systems in CT for Lung pathology COVID based
CH 21: A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.
CH 1: Lung Segmentation using Lung X-ray Scans: U-Series
CH 2: Lung Classification using Lung X-ray Scans
CH 3: Heatmap using Explainable AI on Lung X-ray Scans
CH 4: Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
CH 5: Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
CH 6: Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
CH 7: Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
CH 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
CH 9: Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
CH 10: Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
CH 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
CH 12: Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
CH 13: Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
CH 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
CH 15: Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
CH 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
CH 17: Lung COVID from pathology to radiological features
CH 18: Lung COVID and pulmonary embolism
CH 19: Classification systems in X-ray for Lung pathology COVID based
CH 20: Classification systems in CT for Lung pathology COVID based
CH 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
CH 1: Lung Segmentation using Lung X-ray Scans: U-Series
CH 2: Lung Classification using Lung X-ray Scans
CH 3: Heatmap using Explainable AI on Lung X-ray Scans
CH 4: Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
CH 5: Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
CH 6: Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
CH 7: Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
CH 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
CH 9: Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
CH 10: Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
CH 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
CH 12: Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
CH 13: Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
CH 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
CH 15: Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
CH 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
CH 17: Lung COVID from pathology to radiological features
CH 18: Lung COVID and pulmonary embolism
CH 19: Classification systems in X-ray for Lung pathology COVID based
CH 20: Classification systems in CT for Lung pathology COVID based
CH 21: A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.
CH 1: Lung Segmentation using Lung X-ray Scans: U-Series
CH 2: Lung Classification using Lung X-ray Scans
CH 3: Heatmap using Explainable AI on Lung X-ray Scans
CH 4: Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
CH 5: Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
CH 6: Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
CH 7: Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
CH 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
CH 9: Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
CH 10: Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
CH 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
CH 12: Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
CH 13: Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
CH 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
CH 15: Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
CH 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
CH 17: Lung COVID from pathology to radiological features
CH 18: Lung COVID and pulmonary embolism
CH 19: Classification systems in X-ray for Lung pathology COVID based
CH 20: Classification systems in CT for Lung pathology COVID based
CH 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.
