Buch, Englisch, 458 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 970 g
Buch, Englisch, 458 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 970 g
ISBN: 978-0-12-810408-8
Verlag: William Andrew Publishing
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.
Zielgruppe
<p>Academic and industry researchers and graduate students in medical imaging, computer vision, and biomedical engineering.</p>
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
Weitere Infos & Material
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