S. Kevin Zhou, PhD is dedicated to research on medical image computing, especially analysis and reconstruction, and its applications in real practices. Currently, he is a Distinguished Professor and Founding Executive Dean of School of Biomedical Engineering, University of Science and Technology of China (USTC) and directs the Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE). Dr. Zhou was a Principal Expert and a Senior R&D Director at Siemens Healthcare Research. He has been elected as a fellow of AIMBE, IAMBE, IEEE, MICCAI and NAI and serves the MICCAI society as a board member and treasurer.. Hayit Greenspan, PhD is focused on developing deep learning tools for medical image analysis, as well as their translation to the clinic. She is a Professor of Biomedical Engineering with the Faculty of Engineering at Tel-Aviv University (on Leave), and currently with the Department of Radiology and the AI and Human Health Department at the Icahn School of Medicine at Mount Sinai, NYC. She is the Director of the AI Core at the Biomedical Engineering and Imaging (BMEII) Institute and the Co-director of a new AI and emerging technologies PhD program at Mount Sinai. Dr. Greenspan is also a co-founder of RADLogics Inc., a startup company bringing AI tools to clinician support Dinggang Shen, PhD is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, AIMBE, IAPR and MICCAI. He was a Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with the University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA. His research interests include medical image analysis, computer vision and pattern recognition. He has published more than 1,500 peer-reviewed papers in the international journals and conference proceedings, with H-index 130 and over 70K citations.

• PART 1: INTRODUCTION1. An introduction to neural network and deep learning (covering CNN, RNN, RBM, Autoencoders)Heung-Il Suk2. An Introduction to Deep Convolutional Neural Nets for Computer VisionSuraj Srinivas, Ravi K. Sarvadevabhatla, Konda R. Mopuri, Nikita Prabhu, Srinivas S.S. Kruthiventi and R. Venkatesh BabuPART 2: MEDICAL IMAGE DETECTION AND RECOGNITION3. Efficient Medical Image ParsingFlorin C. Ghesu, Bogdan Georgescu and Joachim Hornegger4. Multi-Instance Multi-Stage Deep Learning for Medical Image RecognitionZhennan Yan, Yiqiang Zhan, Shaoting Zhang, Dimitris Metaxas and Xiang Sean Zhou5. 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 Liang6. Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical ImagesHao Chen, Qi Dou, Lequan Yu, Jing Qin, Lei Zhao, Vincent C.T. Mok, Defeng Wang, Lin Shi and Pheng-Ann Heng7. Deep Voting and Structured Regression for Microscopy Image AnalysisYuanpu Xie, Fuyong Xing and Lin YangPART 3 MEDICAL IMAGE SEGMENTATION8. Deep Learning Tissue Segmentation in Cardiac Histopathology ImagesJeffrey J. Nirschl, Andrew Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman and Anant Madabhushi9. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch MatchingYanrong Guo, Yaozong Gao and Dinggang Shen10. Characterization of Errors in Deep Learning-Based Brain MRI SegmentationAkshay Pai, Yuan-Ching Teng, Joseph Blair, Michiel Kallenberg, Erik B. Dam, Stefan Sommer, Christian Igel and Mads NielsenPART 4 MEDICAL IMAGE REGISTRATION11. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations LearningShaoyu Wang, Minjeong Kim, Guorong Wu and Dinggang Shen12. Convolutional Neural Networks for Robust and Real-Time 2-D/3-D RegistrationShun Miao, Jane Z. Wang and Rui LiaoPART 5 COMPUTER-AIDED DIAGNOSIS AND DISEASE QUANTIFICATION13. Chest Radiograph Pathology Categorization via Transfer LearningIdit Diamant, Yaniv Bar, Ofer Geva, Lior Wolf, Gali Zimmerman, Sivan Lieberman, Eli Konen and Hayit Greenspan14. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of LesionsGustavo Carneiro, Jacinto Nascimento and Andrew P. Bradley15. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer’s DiseaseVamsi K. Ithapu, Vikas Singh and Sterling C. Johnson16. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image SynthesisRaviteja Vemulapalli, Hien Van Nguyen and S.K. Zhou17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep LearningHoo-Chang Shin, Le Lu and Ronald M. Summers