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.