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Deep Learning Models for Medical Imaging is suitable for computer science, medical imaging and biomedical engineering researchers and students who need up-to-date deep learning tools to apply to medical image analysis problems. The book presents deep learning concepts and modeling as applied to medical imaging and/or healthcare, using two different real-world case studies, providing complete implementation (via GitHub) of both standard (e.g. LeNet, Alexnet, VGGNet, ResNet and InceptionNet) and recent models (Mobile net and squeeze-and excitation net).
- Provides a step-by-step approach to develop deep learning models
- Presents case studies showing end-to-end implementation
- Includes codes provided in GitHub
Engineers and biomedical engineers, medical imaging researchers and graduate students
1.1 Basics to deep learning 1.2 Importance of deep learning 1.3 Deep learning on healthcare/medical images 1.3.1 Medical imaging scope 1.3.2 Deep learning on medical imaging: a quick review 1.4 Scope of the book
2 Deep learning: a review
2.1 Artificial neural networks 2.1.1 The neuron 2.1.2 The role of non-linearity 2.1.3 Multi-layer Perceptrons 2.1.4 Backpropagation 2.1.5 Optimization 2.2 Convolutional neural networks 2.2.1 Convolutions 2.2.2 Pooling 2.2.3 Activation Functions 2.2.4 Batch Normalization 2.2.5 Output Layer 2.3 Encoder-decoder architectures 2.3.1 Auto-encoders 2.3.2 Multi-scale feature propagation
3 Deep learning models
3.1 Types: a review 3.2 Evolution of deep learning models 3.2.1 Classification 3.2.2 Localization
3.2.3 Segmentation 3.2 Deep learning model comparison
4 Case study I: Histopathology images
5 Case study II: Chest radiographs
6. Other medical imaging issues
- No. of pages:
- © Academic Press 2021
- 1st April 2021
- Academic Press
- Paperback ISBN:
Dr. Santosh (IEEE Senior Memmber) is an Assistant Professor and Graduate Program Director of the Department of Computer Science at the University of South Dakota (USD). Also, Dr. Santosh serves School of Computing and IT, Taylor's University as a Visiting Associate Professor. Before joining USD, Dr. Santosh worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research centre, Universite de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also worked as a research scientist at the INRIA Nancy Grand Est research centre, France, where, he has received his PhD diploma in Computer Science. Dr. Santosh demonstrated expertise in artificial intelligence, machine learning, pattern recognition, computer vision, image processing, data mining, and big data with various application domains, such as healthcare informatics and medical imaging, graphics recognition, document information content exploitation, biometrics, forensics, speech analysis, satellite imaging, robotics, and Internet of things. He is the recipient of the 2019 President’s Award for Research Excellence (USD) and HHS Ignite Award from the US Department of Health & Human Services (2014). He published more than 120 peer-reviewed research articles, authored three books, and edited more than 10 journal issues and conference proceedings. Dr. Santosh serves as an associate editor for multiple journals, such as Int. J of Machine Learning & Cybernetics (Springer) and IEEE Access, and chaired six-plus international conference events in the domain
Department of Computer Science at the University of South Dakota (USD)
Nibaran Das received his B. Tech in Computer Science and Technology from Kalyani University, MC SE and Ph.D. (Engineering) degrees from Jadavpur University in 2003, 2005 and 2012 respectively. He joined in Computer Science and Engineering Department of Jadavpur University in 2006. Dr. Das has published more than 120+ research articles in various international journals, conference proceedings and book chapters in the areas of pattern recognition and image analysis using different Machine Learning and Deep Learning techniques. He has co-authored three books. He has supervised around 30+ Master degree students till date. He has been a member of the advisory/program/organizing committees of different reputed International conferences and workshops. He served as the Chairperson of the Young Professional Affinity Group of IEEE Kolkata Section in the last two years.
Department of Computer Science and Engineering, Jadavpur University, Kolkota, India
Swarnendu Ghosh works at Jadavpur University in Kolkota, India.
Jadavpur University, Kolkota, India
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