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Deep Learning and Convolutional Neural Networks for Medical Image Computing : Precision Medicine, High Performance and Large-Scale Datasets / / edited by Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang
| Deep Learning and Convolutional Neural Networks for Medical Image Computing : Precision Medicine, High Performance and Large-Scale Datasets / / edited by Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIII, 326 p. 117 illus., 100 illus. in color.) |
| Disciplina | 006.32 |
| Collana | Advances in Computer Vision and Pattern Recognition |
| Soggetto topico |
Optical data processing
Artificial intelligence Neural networks (Computer science) Radiology Image Processing and Computer Vision Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks Imaging / Radiology |
| ISBN | 3-319-42999-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. |
| Record Nr. | UNINA-9910254815703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics / / edited by Le Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang
| Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics / / edited by Le Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 461 p. 177 illus., 156 illus. in color.) |
| Disciplina |
006.6
006.37 006.32 |
| Collana | Advances in Computer Vision and Pattern Recognition |
| Soggetto topico |
Optical data processing
Radiology Artificial intelligence Neural networks (Computer science) Image Processing and Computer Vision Imaging / Radiology Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 3-030-13969-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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?. |
| Record Nr. | UNINA-9910349280203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||