| Description |
1 online resource (172 p.). |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
Primers in Biomedical Imaging Devices and Systems Ser. |
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Primers in Biomedical Imaging Devices and Systems Ser.
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| Note |
Description based upon print version of record. |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Note |
4.7.3.2 Results with data augmentation. |
| Subject |
Machine learning.
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Diagnostic imaging.
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Artificial intelligence -- Medical applications.
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Deep learning (Machine learning)
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Diagnostic Imaging |
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Deep Learning |
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Machine Learning |
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Apprentissage automatique.
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Imagerie pour le diagnostic.
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Intelligence artificielle en médecine.
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Apprentissage profond.
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Artificial intelligence -- Medical applications
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Diagnostic imaging
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Machine learning
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| Added Author |
Das, Nibaran.
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Ghosh, Swarnendu.
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| Other Form: |
Print version: Santosh, K. C. Deep Learning Models for Medical Imaging San Diego : Elsevier Science & Technology,c2021 9780128235041 |
| ISBN |
9780128236505 |
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0128236507 |
| Standard No. |
AU@ 000069968191 |
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