Deep Learning Models for Medical Imaging
Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available...
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Chantilly
Elsevier Science & Technology
2021
Academic Press |
| Ausgabe: | 1 |
| Schriftenreihe: | Primers in Biomedical Imaging Devices and Systems |
| Schlagworte: | |
| ISBN: | 9780128235041, 0128235047 |
| Online-Zugang: | Volltext |
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| Abstract | Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. |
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| AbstractList | Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. |
| Author | Das, Nibaran Ghosh, Swarnendu Santosh, K. C |
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| DOI | 10.1016/B978-0-12-823504-1.00002-7 |
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| Discipline | Medicine |
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| PublicationSeriesTitle | Primers in Biomedical Imaging Devices and Systems |
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| Snippet | Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two... |
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| SubjectTerms | Artificial intelligence Artificial intelligence-Medical applications Diagnostic imaging |
| TableOfContents | Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning -- 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures -- 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models -- 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology -- 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning -- 4.7.3.2 Results with data augmentation -- 4.7.3.3 Results using ensemble of classifiers -- 4.7.4 Summary -- References -- 5 COVID-19: prediction, screening, and decision-making -- 5.1 Background -- 5.2 Predictive modeling and infectious disease outbreaks -- 5.3 Need of medical imaging tools for COVID-19 outbreak screening -- 5.4 Deep neural networks for COVID-19 screening -- 5.4.1 Truncated Inception Net: COVID-19 outbreak screening using chest X-rays [7] -- 5.4.2 Shallow CNN for COVID-19 outbreak screening using chest X-rays [2] -- 5.4.3 DNN to detect COVID-19: one architecture for both chest CT and X-ray images [3] -- 5.5 Discussion: how big data is big? -- References -- Index -- Back Cover |
| Title | Deep Learning Models for Medical Imaging |
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