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Deep Learning and Data Labeling for Medical Applications

and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28;

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Data Analytics in Biomedical Engineering and Healthcare

medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data;

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Deep Learning for Data Analytics

of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications;

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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for;

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Deep Learning for Biomedical Applications

future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a;

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Deep Learning for Medical Decision Support Systems

the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience;

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Deep Learning for Medical Decision Support Systems

the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience;

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Deep Learning in Healthcare

current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical;

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Deep Learning in Healthcare

current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical;

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Handbook of Deep Learning in Biomedical Engineering

-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and;

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Deep Learners and Deep Learner Descriptors for Medical Applications

utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2;

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Introduction to Deep Learning for Healthcare

multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for;

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Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based;

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Deep Learning for Cancer Diagnosis

. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more;

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Deep Learning for Cancer Diagnosis

. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more;

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Deep Learning and Edge Computing Solutions for High Performance Computing

This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The;

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Deep Learning for the Life Sciences

sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics;

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Deep Learning for Medical Image Analysis

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This;

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Deep Biometrics

biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize;

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Deep Biometrics

biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize;

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State of the Art in Neural Networks and Their Applications

, optical coherence tomography, cryo-electron tomography, 3D MRI, CT, and more Covers deep learning for several medical conditions;

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Medical Imaging

recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked;

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Deep Learning in Data Analytics

, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches;

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Deep Network Design for Medical Image Computing

deep learning design principles for MIC and to apply them to their medical problems. Explains design principles of deep learning;

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Deep Learning Techniques and Optimization Strategies in Big Data Analytics

industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear;

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Artificial Intelligence for Data-Driven Medical Diagnosis

healthcare. Medical data from different sources can also be analyzed via Artificial Intelligence techniques for more effective results.;

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Hands-On Natural Language Processing with PyTorch 1.x

Become a proficient NLP data scientist by developing deep learning models for NLP and extract valuable insights from structured and;

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Einde inhoud

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