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• Technologies for implementing AmIHealth environments.- Frameworks related with AmIHealth environments.- Applied algorithms in e-Health systems.- Interactions within the AmIHealth environments.- Applications and case studies of AmIHealth environments.- Metrics for Health environments.
• (source: Nielsen Book Data)

This book constitutes the refereed conference proceedings of the First International Conference on Ambient Intelligence for Health, AmIHEALTH 2015, held in Puerto Varas, Chile, in December 2015. The 20 revised full papers and 9 short papers were reviewed and selected from 32 submissions and cover topics on technologies for implementing AmIHealth environments; frameworks related with AmIHealth environments; applied algorithms in e-Health systems; interactions within the AmIHealth environments; applications and case studies of AmIHealth environments; and metrics for health environments.
(source: Nielsen Book Data)

• 1.
• Deep Learning for Innovative Medical Decision Support
• 2.
• Deep Learning and Image Analysis for Medical Decision Support
• 3.
• Deep Learning Oriented Systems for Medical Education
• 4.
• Hybrid Deep Systems for Medical Education and Decision Support
• 5.
• Deep Learning and Optimization for Medical Education and Decision Support 6.
• Deep Learning and Multimedia for Medical Education and Decision Support
• 7.
• Deep Learning and Traditional Methods for Medical Education and Decision Support.
• (source: Nielsen Book Data)

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today's problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation. .
(source: Nielsen Book Data)

• Blood-flow estimation in the hepatic arteries based on 3D/2D angiography registration.- Automated quantification of blood flow velocity from time-resolved CT angiography.- Multiple device segmentation for fluoroscopic imaging using multi-task learning.- Segmentation of the Aorta Using Active Contours with Histogram-Based Descriptors.- Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Network.- Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts.- Deep Learning-based Detection and Segmentation for BVS Struts in IVOCT Images.- Towards Automatic Measurement of Type B Aortic Dissection Parameters.- Prediction of FFR from IVUS Images using Machine Learning.- Deep Learning Retinal Vessel Segmentation From a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks.- An Efficient and Comprehensive Labeling Tool for Large-scale Annotation of Fundus Images.- Crowd disagreement about medical images is informative.- Imperfect Segmentation Labels: How Much Do They Matter?.- Crowdsourcing annotation of surgical instruments in videos of cataract surgery.- Four-dimensional ASL MR angiography phantoms with noise learned by neural styling.- Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans.- Capsule Networks against Medical Imaging Data Challenges.- Fully Automatic Segmentation of Coronary Arteries based on Deep Neural Network in Intravascular Ultrasound Images.- Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos.- Radiology Objects in COntext (ROCO).- Improving out-of-sample prediction of quality of MRIQC.
• (source: Nielsen Book Data)

This book constitutes the refereed joint proceedings of the 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the Third International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 9 full papers presented at CVII-STENT 2017 and the 12 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing.
(source: Nielsen Book Data)

• Intro
• Additional Workshop Editors
• MLCN 2018 Preface
• DLF 2018 Preface
• iMIMIC 2018 Preface
• Organization
• Contents
• First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018
• Alzheimer's Disease Modelling and Staging Through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
• 1 Introduction
• 2 Method
• 3 Results
• 3.1 Benchmark on Synthetic Data
• 3.2 Application on Real Data
• 4 Conclusion
• References

• Multi-channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
• 1 Introduction
• 2 Method
• 2.1 Multi-channel Variational Inference
• 2.2 Gaussian Linear Case
• 3 Experiments
• 3.1 Experiments on Linearly Generated Synthetic Datasets
• 3.2 Application to Clinical and Medical Imaging Data in AD
• 4 Discussion and Conclusion
• References
• Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer's Disease
• 1 Introduction
• 2 Related Work
• 2.1 Alzheimer Classification
• 2.2 Visualization Methods

• 3 Methods
• 3.1 Data
• 3.2 Model
• 3.3 Visualization Methods
• 4 Results
• 4.1 Classification
• 4.2 Relevant Brain Areas
• 4.3 Differences Between Visualization Methods
• 5 Conclusion
• References
• Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data
• 1 Introduction
• 1.1 Multi-site Data and Batch Effects
• 2 Machine Learning and Functional Connectivity Graphs
• 3 Batch Effects Correction Techniques
• 3.1 Adding Site as Covariate
• 3.2 Z-Score Normalization
• 3.3 Whitening
• 3.4 Solving Linear Transformations
• 4 Experiments and Results

• 4.1 Dataset
• 4.2 Experiments and Results
• 5 Discussion
• References
• First International Workshop on Deep Learning Fails Workshop, DLF 2018
• Towards Robust CT-Ultrasound Registration Using Deep Learning Methods
• 1 Introduction
• 2 Methods
• 3 Data
• 3.1 Clinical Data
• 3.2 Training Data
• 4 Experiments
• 4.1 Mono-Modal
• 4.2 Multi-modal (Simulated)
• 4.3 Inaccurate Ground Truth
• 4.4 CT-US
• 5 Discussion and Conclusion
• References
• To Learn or Not to Learn Features for Deformable Registration?
• 1 Introduction
• 2 Method
• 2.1 Discrete Optimization

• 2.2 Deep Learning Framework
• 3 Experiments and Results
• 3.1 Datasets Description
• 3.2 Evaluation Metric
• 3.3 Implementation Detail
• 3.4 Feature Learning Experiments and Results
• 4 Conclusions
• References
• Evaluation of Strategies for PET Motion Correction
• Manifold Learning vs. Deep Learning
• 1 Introduction
• 2 Methods
• 2.1 Network Architecture
• 2.2 Training Details
• 3 Experiments
• 3.1 Synthetic Dataset
• 3.2 Comparison Method: Data-Driven Gating
• 3.3 Assessment of Corrected Volume Quality
• 4 Discussion and Conclusions
• References

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis. .
(source: Nielsen Book Data)

• From Large to Small Organ Segmentation in CT Using Regional Context.- Motion Corruption Detection in Breast DCE-MRI.- Detection and Localization of Drosophila Egg Chambers in Microscopy Images.- Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-specific Coronary Calcium Scoring.- Atlas of Classifiers for Brain MRI Segmentation.- Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis.- Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease.- Multi-Factorial Age Estimation from Skeletal and Dental MRI Volumes.- Automatic Classification of Proximal Femur Fractures Based on Attention Models.- Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation.- Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble.- STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion.- Classification of Alzheimer's Disease by Cascaded Convolutional Neural Networks Using PET Images.- Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images.- Multi-Scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base.- Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-Status Dementia Diagnosis.- 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels.- Efficient Groupwise Registration for Brain MRI by Fast Initialization.- Sparse Multi-View Task-centralized Learning for ASD Diagnosis.- Inter-Subject Similarity Guided Brain Network Modelling for MCI Diagnosis.- Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data.- Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images.- Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity.- Gradient Boosted Trees for Corrective Learning.- Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis.- A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling.- Collage CNN for Renal Cell Carcinoma Detection from CT.- Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images.- Localizing Cardiac Structures in Fetal Heart Ultrasound Video.- Deformable Registration Through Learning of Context-Specific Metric Aggregation.- Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-learning Based Cascade Framework.- 3D U-net with Multi-Level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images.- Indecisive Trees for Classification and Prediction of Knee Osteoarthritis.- Whole Brain Segmentation and Labeling from CT using synthetic MR Images.- Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification.- Fusion of High-order and Low-order Effective Connectivity Networks for MCI Classification.- Novel Effective Connectivity Network Inference for MCI Identification.- Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network.- Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to "Virtual" High-Dose CT Images.- Deep-Fext: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction.- Product Space Decompositions for Continuous Representations of Brain Connectivity.- Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks.- Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging.- Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks.
• (source: Nielsen Book Data)

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 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

• 2.2 Kernel-Radius-Based Feature Extraction Method
• 3 Experiments
• 3.1 Database
• 3.2 The Experimental Results and Discussions
• 4 Conclusion
• References
• Some Directions of Medical Informatics in Russia
• Abstract
• 1 Gelfand's Approach to Medical Informatics
• 1.1 Personal Applicability of the Result
• 1.2 Using the Experience of a Doctor
• 1.3 Proof of the Result
• 2 Medical Information System "Transfusiology"
• 3 Time-Oriented Multi-image Case History
• Way to the "Disease Image" Analysis
• Acknowledgements
• References

• A Computer Simulation Approach to Reduce Appointment Lead-Time in Outpatient Perinatology Department ...
• Abstract
• 1 Introduction
• 2 Related Work
• 3 Modeling the Perinatology Department: A Case Study in a Maternal-Child Hospital
• 4 Conclusions and Future Work
• References
• Engaging Patients, Empowering Doctors in Digitalization of Healthcare
• Abstract
• 1 Introduction
• 2 Methods and Discussion
• 3 Conclusion
• Acknowledgements
• References
• Developing a Tunable Q-Factor Wavelet Transform Based Algorithm for Epileptic EEG Feature Extraction
• Abstract
• 1 Introduction

• 2 Datasets and Methods
• 2.1 Experimental Data
• 2.2 Methodology
• 3 Performance Measurements
• 4 Experimental Results and Discussions
• 5 Conclusions
• References
• Granular Computing Combined with Support Vector Machines for Diagnosing Erythemato-Squamous Diseases
• 1 Introduction
• 2 The Basic Knowledge of GrC and SVM
• 2.1 The Basic Idea of GrC
• 2.2 Support Vector Machines
• 3 GrC and SVM Based Feature Selection Algorithm
• 3.1 Feature search strategies
• 3.2 Search for Best Parameters
• 3.3 GrC Combined SVM Feature Selection Algorithm
• 4 Experiments and Results

• 4.1 The Erythemato-Squamous Diseases Dataset
• 4.2 Experimental Results and Analysis
• 5 Conclusions
• References
• A Semantically-Enabled System for Inflammatory Bowel Diseases
• 1 Introduction
• 2 System
• 3 Semantic Queries
• 4 Experiments
• 4.1 Basic Information
• 4.2 IBD Onset Distribution
• 4.3 Season and Smoking Factors Analysis
• 5 Related Research
• 6 Conclusions
• References
• Early Classification of Multivariate Time Series Based on Piecewise Aggregate Approximation
• Abstract
• 1 Introduction
• 2 Background
• 3 Related Work

This book constitutes the refereed proceedings of the 6th International Conference on Health Information Science, HIS 2017, held in Moscow, Russia, in October 2017. The 11 full papers and 7 short papers presented were carefully reviewed and selected from 44 submissions. The papers feature multidisciplinary research results in health information science and systems that support health information management and health service delivery. They relate to all aspects of the conference scope, such as medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain; data management, data mining, and knowledge discovery, management of publichealth, examination of standards, privacy and security issues; computer visualization and artificial intelligence for computer aided diagnosis; development of new architectures and applications for health information systems.
(source: Nielsen Book Data)

• Analysis on manifolds.- Shape analysis.- Disease diagnosis/progression.- Brain networks an connectivity.- Diffusion imaging.- Quantitative imaging.- Imaging genomics.- Image registration.- Segmentation.- General image analysis. <.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, held at the Appalachian State University, Boon, NC, USA, in June 2017. The 53 full papers presented in this volume were carefully reviewed and selected from 147 submissions. They were organized in topical sections named: analysis on manifolds; shape analysis; disease diagnosis/progression; brain networks an connectivity; diffusion imaging; quantitative imaging; imaging genomics; image registration; segmentation; general image analysis.
(source: Nielsen Book Data)

• Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases.- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases.- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images.- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images.- Inferring Disease Status by non-Parametric Probabilistic Embedding.- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images.- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study.- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker.- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation.- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images.- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features.- Representation Learning for Cross-Modality Classification.- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound.- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images.- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data.- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields.- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data.- Non-local Graph-based Regularization for Deformable Image Registration.- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. .
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big data" algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis.
(source: Nielsen Book Data)

• Brain Lesion.- Brain Tumor Segmentation (BRATS).- Ischemic Stroke Lesion Image Segmentation (ISLES), Mild Traumatic Brain Injury Outcome Prediction (mTOP).
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion, as well as the challenges on Brain Tumor Segmentation (BRATS), Ischemic Stroke Lesion Image Segmentation (ISLES), and the Mild Traumatic Brain Injury Outcome Prediction (mTOP), held in Athens, October 17, 2016, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 26 papers presented in this volume were carefully reviewed. They present the latest advances in segmentation, disease prognosis and other applications to the clinical context.
(source: Nielsen Book Data)

• Spectral methods
• Longitudinal methods
• Shape methods.

This book constitutes the refereed post-conference proceedings of the First International Workshop on Spectral and Shape Analysis in Medical Imaging, SeSAMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 10 submitted full papers presented in this volume were carefully reviewed. The papers reflect the following topics: spectral methods; longitudinal methods; and shape methods.

• Brain Lesion Image Analysis
• Brain Tumor Image Segmentation
• Combined MRI and Pathology Brain Tumor Classification
• Tools Allowing Clinical Translation of Image Computing Algorithms.

The two-volume set LNCS 11992 and 11993 constitutes the thoroughly refereed proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algorithms (TACTICAL). These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Shenzhen, China, in October 2019. The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (12 selected papers from 32 submissions); brain tumor image segmentation (58 selected papers from 102 submissions); combined MRI and pathology brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.).

• Brain lesion image analysis
• Brain tumor image segmentation
• Ischemic stroke lesion image segmentation.

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion (BrainLes), Brain Tumor Segmentation (BRATS) and Ischemic Stroke Lesion Segmentation (ISLES), held in Munich, Germany, on October 5, 2015, in conjunction with the International Conference on Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015. The 25 papers presented in this volume were carefully reviewed and selected from 28 submissions. They are grouped around the following topics: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation.

• Fundamental methods for image-based biophysical modeling and image synthesis
• Biophysical and data-driven models of disease progression or organ development
• Biophysical and data-driven models of organ motion and deformation
• Biophysical and data-driven models of image formation and acquisition
• Segmentation/registration across or within modalities to aid the learning of model parameters
• Cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis
• Simulation and synthesis from large-scale image databases
• Automated techniques for quality assessment of simulations and synthetic images
• Image registration and segmentation
• Image denoising and information fusion
• Image reconstruction from sparse data or sparse views
• Real-time simulation of biophysical properties
• Simulation based approaches for medical imaging
• Synthesis and its applications in computational medical imaging.

This book constitutes the refereed proceedings of the First International Workshop on Simulation and Synthesis in Medical Imaging, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 revised full papers presented together in this book were carefully reviewed and selected from 21 submissions. The contributions span the following broad categories: fundamental methods for image-based biophysical modeling and image synthesis; biophysical and data-driven models of disease progression or organ development; biophysical and data-driven models of organ motion and deformation; biophysical and data-driven models of image formation and acquisition; segmentation/registration across or within modalities to aid the learning of model parameters; cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis; simulation and synthesis from large-scale image databases; automated techniques for quality assessment of simulations and synthetic images; as well as several applications of image synthesis and simulation in medical imaging such as image registration and segmentation; image denoising and information fusion; image reconstruction from sparse data or sparse views; and real-time simulation of biophysical properties. The papers were divided into two general topics named "simulation based approaches for medical imaging" and "synthesis and its applications in computational medical imaging."

• Brain Lesion Image Analysis
• Brain Tumor Image Segmentation
• Combined MRI and Pathology Brain Tumor Classification
• Tools Allowing Clinical Translation of Image Computing Algorithms.

The two-volume set LNCS 11992 and 11993 constitutes the thoroughly refereed proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algorithms (TACTICAL). These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Shenzhen, China, in October 2019. The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (12 selected papers from 32 submissions); brain tumor image segmentation (58 selected papers from 102 submissions); combined MRI and pathology brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.).

• Sensor data synchronization in a IoT environment for infants motricity measurement.- A Real-time Algorithm for PPG Signal Processing During Intense Physical Activity.- Design and Testing of a Textile EMG Sensor for Prosthetic Control.- Design of a smart mechatronic system to combine garments for blind people: first insights IoT for Health applications and solutions.- Towards a smartwatch for cu-less blood pressure measurement using PPG signal and physiological features.- WiFi-enabled Automatic Eating Moment Monitoring Using Smartphones.- SocialBike: Quantified-self Data as Social Cue in Physical Activity.- Assisting Radiologists in X-Ray Diagnostics Design and Evaluation for Digital Forensic Ready Wireless Medical Systems.- An IoT-based Healthcare Ecosystem for Home Intelligent Assistant Services in Smart Homes.
• .
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 6th International Conference on Internet of Things (IoT) Technologies for HealthCare, HealthyIoT 2019, held in Braga, Portugal, in December 2019. The IoT as a set of existing and emerging technologies, notions and services can provide many solutions to delivery of electronic healthcare, patient care, and medical data management. The 10 revised full papers presented were carefully reviewed and selected from 26 submissions. The papers cover topics such as healthcare information systems, consumer health, health informatics, engineering, telecommunications, mathematics and statistics, life and medical sciences, and cloud computing.
(source: Nielsen Book Data)

• Image Reconstruction.- Improving Amide Proton Transfer-weighted MRI Reconstruction using T2-weighted Images.- Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations.- Active MR k-space Sampling with Reinforcement Learning.- Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts.- Joint reconstruction and bias field correction for undersampled MR imaging.- Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping.- End-to-End Variational Networks for Accelerated MRI Reconstruction.- 3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning.- MRI Image Reconstruction via Learning Optimization Using Neural ODEs.- An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms.- Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images using a GAN.- T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions.- Learned Proximal Networks for Quantitative Susceptibility Mapping.- Learning A Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction.- Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography.- Acceleration of High-resolution 3D MR Fingerprinting via a Graph Convolutional Network.- Deep Attentive Wasserstein Generative Adversarial Network for MRI Reconstruction with Recurrent Context-Awareness.- Learning MRI $k$-Space Subsampling Pattern using Progressive Weight Pruning.- Model-driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image.- Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning.- Prediction and Diagnosis.- MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response.- M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients.- Automatic Detection of Free Intra-Abdominal Air in Computed Tomography.- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data.- Geodesically Smoothed Tensor Features for Pulmonary Hypertension Prognosis using the Heart and Surrounding Tissues.- Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution.- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Dynamic Contrast-Enhanced CT Imaging.- Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions.- Feature-enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon cancer.- Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI.- CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis.- Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN.- Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks.- Cross-Domain Methods and Reconstruction.- Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation.- Dynamic memory to alleviate catastrophic forgetting in continuous learning settings.- Unlearning Scanner Bias for MRI Harmonisation.- Cross-Domain Image Translation by Shared Latent Gaussian Mixture Model.- Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy.- X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph.- Domain Adaptation for Ultrasound Beamforming.- CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction.- Domain Adaptation.- Improve Unseen Domain Generalization via Enhanced Local Color Transformation and Augmentation.- Transport-based Joint Distribution Alignment for Multi-site Autism Spectrum Disorder Diagnosis using Resting-state fMRI.- Automatic and interpretable model for periodontitis diagnosis in panoramic radiographs.- Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening.- Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains.- Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes.- Unsupervised Graph Domain Adaptation for Neurodevelopmental Disorders Diagnosis.- JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering.- MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint.- Machine Learning Applications.- Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment.- Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions.- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect.- Chest X-ray Report Generation through Fine-Grained Label Learning.- Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time.- A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction.- Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications.- Large-scale inference of liver fat with neural networks on UK Biobank body MRI.- BUNET: Blind Medical Image Segmentation Based on Secure UNET.- Temporal-consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape.- Decision Support for Intoxication Prediction Using Graph Convolutional Networks.- Latent-Graph Learning for Disease Prediction.- Generative Adversarial Networks.- BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification.- Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement.- Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN.- GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI.- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network.- AGAN: An Anatomy Corrector Conditional Generative Adversarial Network.- SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI.- Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation.- Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields.- Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation.- Graded Image Generation Using Stratified CycleGAN.- Prediction of Plantar Shear Stress Distribution by Conditional GAN with Attention Mechanism.
• (source: Nielsen Book Data)

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography.
(source: Nielsen Book Data)

• Image Segmentation.- Image Registration, Reconstruction and Enhancement.- Radiomics, Predictive Models, and Quantitative Imaging Biomarkers.- Ocular Imaging Analysis.- Biomedical Simulation and Modelling.
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This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 16th Conference on Artificial Intelligence in Medicine, AIME 2017, held in Vienna, Austria, in June 2017. The 21 revised full and 23 short papers presented were carefully reviewed and selected from 113 submissions. The papers are organized in the following topical sections: ontologies and knowledge representation; Bayesian methods; temporal methods; natural language processing; health care processes; and machine learning, and a section with demo papers. .
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• Medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain
• Data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues
• Computer visualization and artificial intelligence for computer aided diagnosis; development of new architectures and applications for health information systems.

This book constitutes the refereed proceedings of the 5th International Conference on Health Information Science, HIS 2016, held in Shanghai, China, in November 2016. The 13 full papers and 9 short papers presented were carefully reviewed and selected from numerous submissions. The scope of the papers includes medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain; data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues; computer visualization and artificial intelligence for computer aided diagnosis; development of new architectures and applications for health information systems.