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E-raamat: Deep Learning Models for Medical Imaging

(KCs PAMI: Pattern Analysis & Machine Intelligence Research Lab - Department of Computer Science, University of South Dakota, USA), (Department of Computer Science and Engineering, Jadavpur University, Kolkota, India), (Jadavpur Univer)
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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.
List of figures
ix
List of tables
xiii
Authors xv
KC Santosh xv
Nibaran Das xv
Swarnendu Ghosh xvi
Foreword xvii
Preface xix
Acronyms xxi
Chapter 1 Introduction
1(28)
1.1 Background
1(1)
1.2 Machine learning and its types
2(4)
1.3 Evolution of machine learning
6(4)
1.3.1 Rule-based learning
6(1)
1.3.2 Feature-based learning
7(2)
1.3.3 Representation learning
9(1)
1.4 Basics to deep learning
10(4)
1.4.1 The rise of cybernetics
10(2)
1.4.2 The connectionist movement
12(1)
1.4.3 The onset of deep learning
13(1)
1.4.4 Motivation: deep learning
14(1)
1.5 Importance of deep learning
14(2)
1.6 Deep learning in medical imaging: a review
16(6)
1.6.1 Medical imaging scope
16(4)
1.6.2 Medical imaging data
20(1)
1.6.3 Applications: deep learning in medical imaging
21(1)
1.7 Scope of the book
22(7)
References
23(6)
Chapter 2 Deep learning: a review
29(36)
2.1 Background
29(1)
2.2 Artificial neural networks
30(20)
2.2.1 The neuron
30(2)
2.2.2 Activation functions
32(3)
2.2.3 Multilayer feed forward neural network
35(2)
2.2.4 Training neural networks by back-propagation
37(2)
2.2.5 Optimization
39(4)
2.2.6 Regularization
43(7)
2.3 Convolutional neural networks
50(8)
2.3.1 Feature extraction using convolutions
50(2)
2.3.2 Subsampling
52(1)
2.3.3 Effect of nonlinearity on activation maps
53(1)
2.3.4 Layer design
54(3)
2.3.5 Output layer
57(1)
2.4 Encoder-decoder architecture
58(7)
2.4.1 Unsupervised learning in CNNs
59(1)
2.4.2 Image-to-image translation
60(1)
2.4.3 Localization
60(1)
2.4.4 Multiscale feature propagation
60(1)
References
61(4)
Chapter 3 Deep learning models
65(34)
3.1 Deep learning models
65(4)
3.1.1 Learning different objectives
65(1)
3.1.2 Network structure for CNNs
66(2)
3.1.3 Types of models based on learning strategies
68(1)
3.2 Elements in deep learning pipeline
69(5)
3.2.1 Data preprocessing
69(1)
3.2.2 Model selection
70(2)
3.2.3 Model validation and hyperparameter tuning
72(2)
3.3 Evolution of deep learning models and applications
74(25)
3.3.1 Classification
81(4)
3.3.2 Localization
85(2)
3.3.3 Segmentation
87(7)
References
94(5)
Chapter 4 Cytology image analysis
99(26)
4.1 Background
99(1)
4.2 Cytology: a brief overview
99(1)
4.3 Types of cytology
100(1)
4.4 Cytology slide preparation
100(6)
4.4.1 Aspiration cytology
102(1)
4.4.2 Exfoliative cytology
102(1)
4.4.3 Abrasive cytology
103(1)
4.4.4 Specimen collection
103(1)
4.4.5 Slide preparation
104(1)
4.4.6 Fixation techniques and staining protocol
105(1)
4.5 Cytological process and digitization
106(2)
4.6 Cervical cell cytology
108(1)
4.6.1 Modalities of cervical specimen collection
108(1)
4.6.2 Characteristics of cytomorphology of malignant cells
108(1)
4.7 Experiments
109(16)
4.7.1 Dataset
109(2)
4.7.2 Experimental setup and protocols
111(1)
4.7.3 Results and discussion
112(8)
4.7.4 Summary
120(1)
References
120(5)
Chapter 5 COVID-19: prediction, screening, and decision-making
125(22)
5.1 Background
125(1)
5.2 Predictive modeling and infectious disease outbreaks
125(7)
5.3 Need of medical imaging tools for COVID-19 outbreak screening
132(1)
5.4 Deep neural networks for COVID-19 screening
133(6)
5.4.1 Truncated Inception Net: COVID-19 outbreak screening using chest X-rays [ 7]
134(1)
5.4.2 Shallow CNN for COVID-19 outbreak screening using chest X-rays [ 2]
135(3)
5.4.3 DNN to detect COVID-19: one architecture for both chest CT and X-ray images [ 3]
138(1)
5.5 Discussion: how big data is big?
139(8)
References
143(4)
Index 147
Prof. KC Santosh is the Chair of the Department of Computer Science at the University of South Dakota (USD). Before joining USD, Prof. Santoshworked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He was a postdoctoral research scientist at the LORIA research centre (with industrial partner, ITESOFT (France)). He has demonstrated expertise in artificial intelligence, machine learning, pattern recognition, computer vision, image processing and data mining with applications, such as medical imaging informatics, document imaging, biometrics, forensics, and speech analysis. His research projects are funded by multiple agencies, such as SDCRGP, Department of Education, National Science Foundation, and Asian Office of Aerospace Research and Development. He is the proud recipient of the Cutler Award for Teaching and Research Excellence (USD, 2021), the Presidents Research Excellence Award (USD, 2019), and the Ignite Award from the U.S. Department Nibaran Das received his B.Tech degree in Computer Science and Technology from Kalyani Govt. Engineering College under KalyaniUniversity, in 2003. He received his M.C.S.E. degree from Jadavpur University, in 2005. He received his Ph.D. (Engg.) degree thereafter from Jadavpur University, in 2012. He joined J.U. as a lecturer in 2006. His areas of current research interest are OCR of handwritten text, optimization techniques, image processing, and deep learning. He has been an editor of Bengali monthly magazine Computer Jagat since 2005. Swarnendu Ghosh is an Assistant Professor at Adamas University in the department of Computer Science and Engineering. He received his B.Tech degree in Computer Science and Engineering from West Bengal University of Technology, in 2012. He received his Masters in Computer Science and Engineering from Jadavpur University, in 2014. He has been a doctoral fellow under the Erasmus Mundus Mobility with Asia at University of Evora, Portugal. Currently he is continuing his Ph.D. on Computer Science and Engineering at Jadavpur University. His area of interest is deep learning, graph based learning, and knowledge representation.
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