Lung Imaging and CADx
portes grátis
Lung Imaging and CADx
El-Baz, Ayman; Suri, Jasjit
Taylor & Francis Ltd
10/2023
404
Mole
Inglês
9781032652566
15 a 20 dias
Descrição não disponível.
Preface, Acknowledgement, Dedication. Chapter 1. Computer Aided Diagnosis of COPD Using Accurate Lung Air Volume Estimation Using CT, Chapter 2. Early detection of COPD: Influence on Lung Cancer Epidemiology, Chapter 3. Dual Energy Computed Tomography for Lung Cancer Diagnosis and Characterisation, Chapter 4. X-Ray Dark-Field Imaging of Lung Cancer in Mice, Chapter 5. Lung Cancer Screening Using Low Dose Computed Tomography, Chapter 6. Computer-aided diagnosis of lung nodules: systems for estimation of lung cancer probability and false positive reduction of lung nodule, Chapter 7. Automated Lung Cancer Detection From PET/CT Images Using Texture and Fractal Descriptors,Chapter 8. Lung cancer risk of population exposed to airborne particles: the contribution of different activities and micro-environments, Chapter 9. Lung Nodule Classification based on the Integration of Higher-Order MGRF Appearance Model and Geometric Features,Chapter 10. Smoking cessation and lung cancer screening programs: The rationale and method to integration,Chapter 11. Automatic Lung Segmentation and Inter-observer Variability Analysis, Chapter 12. Classification of Diseased Lungs using Combination of Riesz and Gabor transforms with Machine Learning, Chapter 13. An Unsupervised Parametric Mixture Model for Automatic Three Dimensional Lung Segmentation, Chapter 14. How Deep Learning is Changing the Landscape of Lung Cancer Diagnosis?,Chapter 15. Early Assessment of Radiation Induced Lung Injury
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Lung Deposited Surface Area;CNN Feature;Data Sets;Cad System;Lung CT Scan;Local Binary Patterns;PFT Measurement;Lung Nodules;Low Dose CT Scan;COPD Patient;Roc Curve;Lung Cancer Screening Program;Lung Segmentation;Ischemic Heart Disease Patients;Phase Contrast Magnetic Resonance Angiography;Morphology Operations;COPD Gold;Indoor Microenvironments;Lung Cancer Detection;Pellet Stoves;Nodule Segmentation;Supermicron Particles;CT Density;SUV Max;False Positive Reduction
Preface, Acknowledgement, Dedication. Chapter 1. Computer Aided Diagnosis of COPD Using Accurate Lung Air Volume Estimation Using CT, Chapter 2. Early detection of COPD: Influence on Lung Cancer Epidemiology, Chapter 3. Dual Energy Computed Tomography for Lung Cancer Diagnosis and Characterisation, Chapter 4. X-Ray Dark-Field Imaging of Lung Cancer in Mice, Chapter 5. Lung Cancer Screening Using Low Dose Computed Tomography, Chapter 6. Computer-aided diagnosis of lung nodules: systems for estimation of lung cancer probability and false positive reduction of lung nodule, Chapter 7. Automated Lung Cancer Detection From PET/CT Images Using Texture and Fractal Descriptors,Chapter 8. Lung cancer risk of population exposed to airborne particles: the contribution of different activities and micro-environments, Chapter 9. Lung Nodule Classification based on the Integration of Higher-Order MGRF Appearance Model and Geometric Features,Chapter 10. Smoking cessation and lung cancer screening programs: The rationale and method to integration,Chapter 11. Automatic Lung Segmentation and Inter-observer Variability Analysis, Chapter 12. Classification of Diseased Lungs using Combination of Riesz and Gabor transforms with Machine Learning, Chapter 13. An Unsupervised Parametric Mixture Model for Automatic Three Dimensional Lung Segmentation, Chapter 14. How Deep Learning is Changing the Landscape of Lung Cancer Diagnosis?,Chapter 15. Early Assessment of Radiation Induced Lung Injury
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Lung Deposited Surface Area;CNN Feature;Data Sets;Cad System;Lung CT Scan;Local Binary Patterns;PFT Measurement;Lung Nodules;Low Dose CT Scan;COPD Patient;Roc Curve;Lung Cancer Screening Program;Lung Segmentation;Ischemic Heart Disease Patients;Phase Contrast Magnetic Resonance Angiography;Morphology Operations;COPD Gold;Indoor Microenvironments;Lung Cancer Detection;Pellet Stoves;Nodule Segmentation;Supermicron Particles;CT Density;SUV Max;False Positive Reduction
