| Titre : |
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 2D/3D MEDICAL IMAGE PROCESSING |
| Type de document : |
texte imprimé |
| Auteurs : |
Rohit, Raja, Auteur ; Sandeep Kumar, Auteur ; Shilpa Rani, Auteur |
| Editeur : |
New York : CRC press |
| Année de publication : |
2021 |
| Importance : |
196p. |
| Présentation : |
couv:ill. |
| Format : |
30cm |
| ISBN/ISSN/EAN : |
978-0-367-37435-8 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Artificial intelligence
Machine learning
Deep learning
Medical image processing
2D medical imaging
3D medical imaging
Image segmentation
Computer-aided diagnosis
Pattern recognition
Digital radiology
Convolutional neural networks
Medical decision support |
| Résumé : |
This book by Rihit Raja explores the critical intersection of artificial intelligence, machine learning, and two-dimensional and three-dimensional medical image processing. The work presents an in-depth analysis of advanced AI techniques applied to solving complex challenges in medical imaging, such as organ segmentation, lesion detection, and tissue characterization.
The author methodically examines various deep neural network architectures tailored for medical image processing, with emphasis on convolutional neural networks and their specialized variants for volumetric 3D data. Clinical case studies illustrate the practical application of these technologies across diverse imaging modalities, including MRI, CT scanning, ultrasound, and radiography.
Particular attention is given to technical aspects such as data preprocessing, augmentation of limited datasets, and strategies to overcome challenges specific to medical data, including heterogeneity and ethical considerations related to their use. The book also addresses recent advances in model interpretability and explainable AI, which are essential for the clinical adoption of these technologies.
Intended for researchers, biomedical engineers, and healthcare professionals working at the interface between technology and medicine, this volume serves as a comprehensive resource on the state-of-the-art applications of artificial intelligence in contemporary medical image analysis and interpretation. |
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN 2D/3D MEDICAL IMAGE PROCESSING [texte imprimé] / Rohit, Raja, Auteur ; Sandeep Kumar, Auteur ; Shilpa Rani, Auteur . - New York : CRC press, 2021 . - 196p. : couv:ill. ; 30cm. ISBN : 978-0-367-37435-8 Langues : Anglais ( eng)
| Mots-clés : |
Artificial intelligence
Machine learning
Deep learning
Medical image processing
2D medical imaging
3D medical imaging
Image segmentation
Computer-aided diagnosis
Pattern recognition
Digital radiology
Convolutional neural networks
Medical decision support |
| Résumé : |
This book by Rihit Raja explores the critical intersection of artificial intelligence, machine learning, and two-dimensional and three-dimensional medical image processing. The work presents an in-depth analysis of advanced AI techniques applied to solving complex challenges in medical imaging, such as organ segmentation, lesion detection, and tissue characterization.
The author methodically examines various deep neural network architectures tailored for medical image processing, with emphasis on convolutional neural networks and their specialized variants for volumetric 3D data. Clinical case studies illustrate the practical application of these technologies across diverse imaging modalities, including MRI, CT scanning, ultrasound, and radiography.
Particular attention is given to technical aspects such as data preprocessing, augmentation of limited datasets, and strategies to overcome challenges specific to medical data, including heterogeneity and ethical considerations related to their use. The book also addresses recent advances in model interpretability and explainable AI, which are essential for the clinical adoption of these technologies.
Intended for researchers, biomedical engineers, and healthcare professionals working at the interface between technology and medicine, this volume serves as a comprehensive resource on the state-of-the-art applications of artificial intelligence in contemporary medical image analysis and interpretation. |
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