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Deep learning models for medical imaging

Santosh, K. C.

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Deep learning models for medical imaging

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning models for medical imaging/ K.C. Santosh, Nibaran Das, Swarnendu Ghosh.
作者:	Santosh, K. C.
其他作者:	Das, Nibaran.
出版者:	San Diego :Elsevier Science & Technology, : 2022.,
面頁冊數:	1 online resource (172 p.)
內容註:	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.
內容註:	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.
內容註:	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.
內容註:	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.
內容註:	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.
內容註:	4.7.3.2 Results with data augmentation.
標題:	Diagnostic imaging. -
電子資源:	https://www.sciencedirect.com/science/book/9780128235041
ISBN:	9780128236505 (electronic bk.)

Deep learning models for medical imaging
Santosh, K. C.

Deep learning models for medical imaging[electronic resource] /K.C. Santosh, Nibaran Das, Swarnendu Ghosh. - San Diego :Elsevier Science & Technology,2022. - 1 online resource (172 p.) - Primers in biomedical imaging devices and systems. - Primers in biomedical imaging devices and systems..

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.

ISBN: 9780128236505 (electronic bk.)Subjects--Topical Terms:

658032
Diagnostic imaging.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: RC78.7.D53 / S35 2022

Dewey Class. No.: 616.0754

Deep learning models for medical imaging
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505 8 $a 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.
505 8 $a 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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505 8 $a 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.
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