Machine Learning and Deep Learning Techniques for Medical Image Recognition, Soufiene, Ben Othman ; Chakraborty, Chinmay
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 66520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 1493938436 ISBN-13(EAN): 9781493938438 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 42230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Автор: K. Kamalanand, B. Thayumanavan, P. Mannar Jawahar Название: Computational Techniques for Dental Image Analysis ISBN: 1522562435 ISBN-13(EAN): 9781522562436 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 236010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With the technology innovations dentistry has witnessed in all its branches over the past three decades, the need for more precise diagnostic tools and advanced imaging methods has become mandatory across the industry. Recent advancements to imaging systems are playing an important role in efficient diagnoses, treatments, and surgeries.Computational Techniques for Dental Image Analysis provides innovative insights into computerized methods for automated analysis. The research presented within this publication explores pattern recognition, oral pathologies, and diagnostic processing. It is designed for dentists, professionals, medical educators, medical imaging technicians, researchers, oral surgeons, and students, and covers topics centered on easier assessment of complex cranio-facial tissues and the accurate diagnosis of various lesions at early stages.
Автор: James Eric Mason; Issa Traor?; Isaac Woungang Название: Machine Learning Techniques for Gait Biometric Recognition ISBN: 331929086X ISBN-13(EAN): 9783319290867 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Introduction.- Background.- Experimental Design and Dataset.- Feature Extraction.-Normalization.- Classification.- Measured Performance.- Experimental Analysis.- Conclusion.
Автор: Guorong Wu; Brent C. Munsell; Yiqiang Zhan; Wenjia Название: Patch-Based Techniques in Medical Imaging ISBN: 3319674331 ISBN-13(EAN): 9783319674339 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the Third International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2017, which was held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 18 regular papers presented in this volume were carefully reviewed and selected from 26 submissions.
Автор: Guorong Wu; Pierrick Coup?; Yiqiang Zhan; Brent Mu Название: Patch-Based Techniques in Medical Imaging ISBN: 3319281933 ISBN-13(EAN): 9783319281933 Издательство: Springer Рейтинг: Цена: 44720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
A Multi-level Canonical Correlation Analysis Scheme for Standard-dose PET Image Estimation.- Image Super-Resolution by Supervised Adaption of Patchwise Self-Similarity from High-Resolution Image.- Automatic Hippocampus Labeling Using the Hierarchy of Sub-Region Random Forests.- Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis.- Improving Accuracy of Automatic Hippocampus Segmentation in Routine MRI by Features Learned from Ultra-high Field MRI.- Dual-Layer l1-Graph Embedding for Semi-Supervised Image Labeling.- Automatic Liver Tumor Segmentation in Follow-up CT Studies Using Convolutional Neural Network.- Block-based Statistics for Robust Non-Parametric Morphometry.- Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-based Edge Detection.- Efficient Lung Cancer Cell Detection with Deep Convolutional Neural Network.- An Effective Approach for Robust Lung Cancer Cell Detection.- Laplacian Shape Editing with Local Patch Based Force Field for Interactive Segmentation.- Hippocampus Segmentation through Distance Field Fusion.- Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compress Sensing.- Fast Regions-of-Interest Detection in Whole Slide Histopathology Images.- Reliability Guided Forward and Backward Patch-based Method for Multi-atlas Segmentation.- Correlating Tumour Histology and ex vivo MRI Using Dense Modality-Independent Patch-Based Descriptor.- Multi-Atlas Segmentation using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression.- A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images.- 3D MRI Denoising using Rough Set Theory and Kernel Embedding Method.- A Novel Cell Orientation Congruence Descriptor for Superpixel based Epithelium Segmentation in Endometrial Histology Images.- Patch-based Segmentation from MP2RAGE Images: Comparison to Conventional Techniques.- Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph.- Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework.- Efficient Multi-Scale Patch-based Segmentation.
Автор: Bjoern Menze; Georg Langs; Le Lu; Albert Montillo; Название: Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging ISBN: 3642366198 ISBN-13(EAN): 9783642366192 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the thoroughly refereed workshop proceedings of the Second International Workshop on Medical Computer Vision, MCV 2012, held in Nice, France, October 2012 in conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012.
Автор: Wenjia Bai; Gerard Sanroma; Guorong Wu; Brent C. M Название: Patch-Based Techniques in Medical Imaging ISBN: 3030004996 ISBN-13(EAN): 9783030004996 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 4th International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018.The 15 full papers presented were carefully reviewed and selected from 17 submissions. The papers are organized in the following topical sections: Image Denoising? Image Registration and Matching, Image Classification and Detection, Brain Image Analysis, and Retinal Image Analysis.
Автор: Le Lu Название: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics ISBN: 3030139689 ISBN-13(EAN): 9783030139681 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Автор: Qian Wang; Yinghuan Shi; Heung-Il Suk; Kenji Suzuk Название: Machine Learning in Medical Imaging ISBN: 3319673882 ISBN-13(EAN): 9783319673882 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging.
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