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This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases.
Dr. Le Lu is the Director of Ping An Technology US Research Labs, and an adjunct faculty member at Johns Hopkins University, USA.Dr. Xiaosong Wang is a Senior Applied Research Scientist at Nvidia Corp., USA.Dr. Gustavo Carneiro is an Associate Professor at the University of Adelaide, Australia.Dr. Lin Yang is an Associate Professor at the University of Florida, USA.
Chapter 1. Clinical Report Guided Multi-Sieving Deep Learning for Retinal Microaneurysm Detection.- Chapter 2. Optic Disc and Cup Segmentation Based on Multi-label Deep Network for Fundus Glaucoma Screening.- Chapter 3. Thoracic Disease Identification and Localization with Limited Supervision.- Chapter 4. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases.- Chapter 5. TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays.- Chapter 6. Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database.- Chapter 7. Deep Reinforcement Learning based Attention to Detect Breast Lesions from DCE-MRI.- Chapter 8. Deep Convolutional Hashing for Low Dimensional Binary Embedding of Histopathological Images.- Chapter 9. Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning.- Chapter 10. Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation.- Chapter 11. Pancreas.- Chapter 12. Multi-Organ.- Chapter 13. Convolutional Invasion and Expansion Networks for Tumor Growth Prediction.- Chapter 14. Cross-Modality Synthesis in Magnetic Resonance Imaging.- Chapter 15. Image Quality Assessment for Population Cardiac MRI.- Chapter 16. Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss.- Chapter 17. Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss.- Chapter 18. Automatic Vertebra Labeling in Large-Scale Medical Images using Deep Image-to-Image Network with Message Passing and Sparsity Regularization.- Chapter 19. 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes.- Chapter 20. Multi-Agent Learning for Robust Image Registration.- Chapter 21. Deep Learning in Magnetic Resonance Imaging of Cardiac Function.- Chapter 22. Automatic Vertebra Labeling in Large-Scale Medical Images using Deep Image-to-Image Network with Message Passing and Sparsity Regularization.- Chapter 23. Deep Learning on Functional Connectivity of Brain: Are We There Yet?.
Z. R. Wang, Weilong Hu, S. J. Yuan, Xiaosong Wang, China) Wang, Z. R. (Harbin Institute of Technology, USA) Hu, Weilong (Troy Design and Manufacturing Co., China) Yuan, S. J. (Harbin Institute of Technology, China) Wang, Xiaosong (Harbin Institute of Technology, Z R Wang, S J Yuan
Danail Stoyanov, Zeike Taylor, Bernhard Kainz, Gabriel Maicas, Reinhard R. Beichel, Anne Martel, Lena Maier-Hein, Kanwal Bhatia, Tom Vercauteren, Ozan Oktay, Gustavo Carneiro, Andrew P. Bradley, Jacinto Nascimento, Hang Min, Matthew S. Brown, Colin Jacobs, Bianca Lassen-Schmidt, Kensaku Mori, Jens Petersen, Raúl San José Estépar, Alexander Schmidt-Richberg, Catarina Veiga
M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu, Joao Manuel R.S. Tavares, Joao Paulo Papa, João Manuel R. S. Tavares