Deep Learning for Medical Image Analysis
Academic Press Inc (Verlag)
978-0-12-810408-8 (ISBN)
- Titel erscheint in neuer Auflage
- Artikel merken
Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
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: 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
| Erscheinungsdatum | 17.02.2017 |
|---|---|
| Reihe/Serie | The MICCAI Society book Series |
| Verlagsort | San Diego |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Gewicht | 970 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Medizin / Pharmazie ► Medizinische Fachgebiete ► Radiologie / Bildgebende Verfahren | |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 0-12-810408-2 / 0128104082 |
| ISBN-13 | 978-0-12-810408-8 / 9780128104088 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
