Deep Learning and Convolutional Neural Networks for Medical Image Computing Precision Medicine, High Performance and Large-Scale Datasets /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Lu, Le (Επιμελητής έκδοσης), Zheng, Yefeng (Επιμελητής έκδοσης), Carneiro, Gustavo (Επιμελητής έκδοσης), Yang, Lin (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Σειρά: Advances in Computer Vision and Pattern Recognition,
• Θέματα: Computer science.; Radiology.; Artificial intelligence.; Image processing.; Neural networks (Computer science).; Computer Science.; Image Processing and Computer Vision.; Artificial Intelligence (incl. Robotics).; Mathematical Models of Cognitive Processes and Neural Networks.; Imaging / Radiology.

This timely text/reference presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural netw...

Πίνακας περιεχομένων:

• Part I: Review
• Chapter 1. Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective
• Chapter 2. Review of Deep Learning Methods in Mammography, Cardiovascular and Microscopy Image Analysis
• Part II: Detection and Localization
• Chapter 3. Efficient False-Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
• Chapter 4. Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning
• Chapter 5. A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set
• Chapter 6. Deep Learning for Histopathological Image Analysis: Towards Computerized Diagnosis on Cancers
• Chapter 7. Interstitial Lung Diseases via Deep Convolutional Neural Networks: Segmentation Label Propagation, Unordered Pooling and Cross-Dataset Learning
• Chapter 8. Three Aspects on Using Convolutional Neural Networks for Computer-Aided Detection in Medical Imaging
• Chapter 9. Cell Detection with Deep Learning Accelerated by Sparse Kernel
• Chapter 10. Fully Convolutional Networks in Medical Imaging: Applications to Image Enhancement and Recognition
• Chapter 11. On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging
• Part III: Segmentation
• Chapter 12. Fully Automated Segmentation Using Distance Regularized Level Set and Deep-Structured Learning and Inference
• Chapter 13. Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms
• Chapter 14. Deep Learning Based Automatic Segmentation of Pathological Kidney in CT: Local vs. Global Image Context
• Chapter 15. Robust Cell Detection and Segmentation in Histopathological Images using Sparse Reconstruction and Stacked Denoising Autoencoders
• Chapter 16. Automatic Pancreas Segmentation Using Coarse-to-Fine Superpixel Labeling
• Part IV: Big Dataset and Text-Image Deep Mining
• Chapter 17. Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database.