Deep Learning for Medical Image Analysis (Paperback)

PART 1: INTRODUCTION

1. An introduction to neural network and deep learning (covering CNN, RNN, RBM, Autoencoders) (Heung-Il Suk)

2. An Introduction to Deep Convolutional Neural Nets for Computer Vision??(Suraj Srinivas, Ravi K. Sarvadevabhatla, Konda R. Mopuri, Nikita Prabhu, Srinivas S.S. Kruthiventi and R. Venkatesh Babu)

PART 2: MEDICAL IMAGE DETECTION AND RECOGNITION

3. Efficient Medical Image Parsing (Florin C. Ghesu, Bogdan Georgescu and Joachim Hornegger)

4. Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition?(Zhennan Yan, Yiqiang Zhan, Shaoting Zhang, Dimitris Metaxas and Xiang Sean Zhou)

5. Automatic Interpretation of Carotid Intima?Media Thickness Videos Using Convolutional Neural Networks? (Nima Tajbakhsh, Jae Y. Shin, R. Todd Hurst, Christopher B. Kendall and Jianming Liang)

6. Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images (Hao Chen, Qi Dou, Lequan Yu, Jing Qin, Lei Zhao, Vincent C.T. Mok, Defeng Wang, Lin Shi and Pheng-Ann Heng)

7. Deep Voting and Structured Regression for Microscopy Image Analysis (Yuanpu Xie, Fuyong Xing and Lin Yang)

PART 3 MEDICAL IMAGE SEGMENTATION

8. Deep Learning Tissue Segmentation in Cardiac Histopathology Images (Jeffrey J. Nirschl, Andrew Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman and Anant Madabhushi)

9. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching (Yanrong Guo, Yaozong Gao and Dinggang Shen)

10. Characterization of Errors in Deep Learning-Based Brain MRI Segmentation (Akshay Pai, Yuan-Ching Teng, Joseph Blair, Michiel Kallenberg, Erik B. Dam, Stefan Sommer, Christian Igel and Mads Nielsen)

PART 4 MEDICAL IMAGE REGISTRATION

11. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning (Shaoyu Wang, Minjeong Kim, Guorong Wu and Dinggang Shen)

12. Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration (Shun Miao, Jane Z. Wang and Rui Liao)

PART 5 COMPUTER-AIDED DIAGNOSIS AND DISEASE QUANTIFICATION

13. Chest Radiograph Pathology Categorization via Transfer Learning (Idit Diamant, Yaniv Bar, Ofer Geva, Lior Wolf, Gali Zimmerman, Sivan Lieberman, Eli Konen and Hayit Greenspan)

14. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions (Gustavo Carneiro, Jacinto Nascimento and Andrew P. Bradley)

15. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer’s Disease (Vamsi K. Ithapu, Vikas Singh and Sterling C. Johnson)

16. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis (Raviteja Vemulapalli, Hien Van Nguyen and S.K. Zhou)

17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning (Hoo-Chang Shin, Le Lu and Ronald M. Summers)

Index