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This book constitutes the refereed proceedings of the First International Workshop on Connectomics in NeuroImaging, CNI 2017, held in conjunction with MICCAI 2017 in Quebec City, Canada, in September 2017. The 19 full papers presented were carefully reviewed and selected from 26 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.
(source: Nielsen Book Data)

• Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017:
• Simultaneous Multiple Surface Segmentation Using Deep Learning / Abhay Shah, Michael D. Abramoff, Xiaodong Wu
• A Deep Residual Inception Network for HEp-2 Cell Classification / Yuexiang Li, Linlin Shen
• Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures / Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan
• Accelerated Magnetic Resonance Imaging by Adversarial Neural Network / Ohad Shitrit, Tammy Riklin Raviv
• Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks / Honghui Liu, Jianjiang Feng, Zishun Feng, Jiwen Lu, Jie Zhou
• 3D Randomized Connection Network with Graph-Based Inference / Siqi Bao, Pei Wang, Albert C. S. Chung
• Adversarial Training and Dilated Convolutions for Brain MRI Segmentation / Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim
• CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography / Shekoufeh Gorgi Zadeh, Maximilian W. M. Wintergerst, Vitalis Wiens, Sarah Thiele, Frank G. Holz, Robert P. Finger et al.
• Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images / S. M. Masudur Rahman Al Arif, Karen Knapp, Greg Slabaugh
• Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images / Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta
• Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks / Sangheum Hwang, Sunggyun Park
• Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms / Fatemeh Taheri Dezaki, Neeraj Dhungel, Amir H. Abdi, Christina Luong, Teresa Tsang, John Jue et al.
• Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks / Matthieu Le, Jesse Lieman-Sifry, Felix Lau, Sean Sall, Albert Hsiao, Daniel Golden
• Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression / Yuru Pei, Yungeng Zhang, Haifang Qin, Gengyu Ma, Yuke Guo, Tianmin Xu et al.
• A Deep Level Set Method for Image Segmentation / Min Tang, Sepehr Valipour, Zichen Zhang, Dana Cobzas, Martin Jagersand
• Context-Based Normalization of Histological Stains Using Deep Convolutional Features / D. Bug, S. Schneider, A. Grote, E. Oswald, F. Feuerhake, J. Schüler et al.
• Transitioning Between Convolutional and Fully Connected Layers in Neural Networks / Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne Martel
• Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks / Behrouz Saghafi, Prabhat Garg, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Ananth J. Madhuranthakam et al.
• Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning / Kim-Han Thung, Pew-Thian Yap, Dinggang Shen
• A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification / William Lotter, Greg Sorensen, David Cox
• Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning / Dheeraj Mundhra, Bharath Cheluvaraju, Jaiprasad Rampure, Tathagato Rai Dastidar
• AGNet: Attention-Guided Network for Surgical Tool Presence Detection / Xiaowei Hu, Lequan Yu, Hao Chen, Jing Qin, Pheng-Ann Heng
• Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker / Kevin George, Adam P. Harrison, Dakai Jin, Ziyue Xu, Daniel J. Mollura
• End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network / Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Marius Staring, Ivana Išgum
• Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images / Azam Hamidinekoo, Reyer Zwiggelaar
• 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation / Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken'ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa et al.
• A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology / Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim
• Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations / Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, M. Jorge Cardoso
• ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features / Inwan Yoo, David G. C. Hildebrand, Willie F. Tobin, Wei-Chung Allen Lee, Won-Ki Jeong
• Fully Convolutional Regression Network for Accurate Detection of Measurement Points / Michal Sofka, Fausto Milletari, Jimmy Jia, Alex Rothberg
• Fast Predictive Simple Geodesic Regression / Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer et al.
• Learning Spatio-Temporal Aggregation for Fetal Heart Analysis in Ultrasound Video / Arijit Patra, Weilin Huang, J. Alison Noble
• Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks / Aleksander Klibisz, Derek Rose, Matthew Eicholtz, Jay Blundon, Stanislav Zakharenko
• Self-supervised Learning for Spinal MRIs / Amir Jamaludin, Timor Kadir, Andrew Zisserman
• Skin Lesion Segmentation via Deep RefineNet / Xinzi He, Zhen Yu, Tianfu Wang, Baiying Lei
• Multi-scale Networks for Segmentation of Brain Magnetic Resonance Images / Jie Wei, Yong Xia
• Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography / Ayelet Akselrod-Ballin, Leonid. Karlinsky, Alon Hazan, Ran Bakalo, Ami Ben Horesh, Yoel Shoshan et al.
• Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections / Adam Porisky, Tom Brosch, Emil Ljungberg, Lisa Y. W. Tang, Youngjin Yoo, Benjamin De Leener et al.
• 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017:
• Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates / Johannes Hofmanninger, Bjoern Menze, Marc-André Weber, Georg Langs
• Automated Detection of Epileptogenic Cortical Malformations Using Multimodal MRI / Ravnoor S. Gill, Seok-Jun Hong, Fatemeh Fadaie, Benoit Caldairou, Boris Bernhardt, Neda Bernasconi et al.
• Prediction of Amyloidosis from Neuropsychological and MRI Data for Cost Effective Inclusion of Pre-symptomatic Subjects in Clinical Trials / Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Didier Dormont, Bruno Dubois et al.
• Automated Multimodal Breast CAD Based on Registration of MRI and Two View Mammography / T. Hopp, P. Cotic Smole, N. V. Ruiter
• EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function / Shikha Chaganti, Jamie R. Robinson, Camilo Bermudez, Thomas Lasko, Louise A. Mawn, Bennett A. Landman
• Erratum to: Fast Predictive Simple Geodesic Regression / Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer et al.

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
(source: Nielsen Book Data)

• International Workshop on Fetal and Infant Image Analysis, FIFI 2017:
• Template-Free Estimation of Intracranial Volume: A Preterm Birth Animal Model Study / Juan Eugenio Iglesias, Sebastiano Ferraris, Marc Modat, Willy Gsell, Jan Deprest, Johannes L. van der Merwe et al.
• Assessing Reorganisation of Functional Connectivity in the Infant Brain / Roxane Licandro, Karl-Heinz Nenning, Ernst Schwartz, Kathrin Kollndorfer, Lisa Bartha-Doering, Hesheng Liu et al.
• Fetal Skull Segmentation in 3D Ultrasound via Structured Geodesic Random Forest / Juan J. Cerrolaza, Ozan Oktay, Alberto Gomez, Jacqueline Matthew, Caroline Knight, Bernhard Kainz et al.
• Fast Registration of 3D Fetal Ultrasound Images Using Learned Corresponding Salient Points / Alberto Gomez, Kanwal Bhatia, Sarjana Tharin, James Housden, Nicolas Toussaint, Julia A. Schnabel
• Automatic Segmentation of the Intracranial Volume in Fetal MR Images / N. Khalili, P. Moeskops, N. H. P. Claessens, S. Scherpenzeel, E. Turk, R. de Heus et al.
• Abdomen Segmentation in 3D Fetal Ultrasound Using CNN-powered Deformable Models / Alexander Schmidt-Richberg, Tom Brosch, Nicole Schadewaldt, Tobias Klinder, Angelo Cavallaro, Ibtisam Salim et al.
• Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning / Caroline Raynaud, Cybèle Ciofolo-Veit, Thierry Lefèvre, Roberto Ardon, Angelo Cavallaro, Ibtisam Salim et al.
• Robust Regression of Brain Maturation from 3D Fetal Neurosonography Using CRNs / Ana I. L. Namburete, Weidi Xie, J. Alison Noble
• 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017:
• Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques / Taibou Birgui Sekou, Moncef Hidane, Julien Olivier, Hubert Cardot
• Detecting Early Choroidal Changes Using Piecewise Rigid Image Registration and Eye-Shape Adherent Regularization / Tiziano Ronchetti, Peter Maloca, Christoph Jud, Christoph Meier, Selim Orgül, Hendrik P. N. Scholl et al.
• Patch-Based Deep Convolutional Neural Network for Corneal Ulcer Area Segmentation / Qichao Sun, Lijie Deng, Jianwei Liu, Haixiang Huang, Jin Yuan, Xiaoying Tang
• Model-Driven 3-D Regularisation for Robust Segmentation of the Refractive Corneal Surfaces in Spiral OCT Scans / Joerg Wagner, Simon Pezold, Philippe C. Cattin
• Automatic Retinal Layer Segmentation Based on Live Wire for Central Serous Retinopathy / Dehui Xiang, Geng Chen, Fei Shi, Weifang Zhu, Xinjian Chen
• Retinal Image Quality Classification Using Fine-Tuned CNN / Jing Sun, Cheng Wan, Jun Cheng, Fengli Yu, Jiang Liu
• Optic Disc Detection via Deep Learning in Fundus Images / Peiyuan Xu, Cheng Wan, Jun Cheng, Di Niu, Jiang Liu
• 3D Choroid Neovascularization Growth Prediction with Combined Hyperelastic Biomechanical Model and Reaction-Diffusion Model / Chang Zuo, Fei Shi, Weifang Zhu, Haoyu Chen, Xinjian Chen
• Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population / Ahmed E. Fetit, Siyamalan Manivannan, Sarah McGrory, Lucia Ballerini, Alexander Doney, Thomas J. MacGillivray et al.
• Non-rigid Registration of Retinal OCT Images Using Conditional Correlation Ratio / Xueying Du, Lun Gong, Fei Shi, Xinjian Chen, Xiaodong Yang, Jian Zheng
• Joint Optic Disc and Cup Segmentation Using Fully Convolutional and Adversarial Networks / Sharath M. Shankaranarayana, Keerthi Ram, Kaushik Mitra, Mohanasankar Sivaprakasam
• Automated Segmentation of the Choroid in EDI-OCT Images with Retinal Pathology Using Convolution Neural Networks / Min Chen, Jiancong Wang, Ipek Oguz, Brian L. VanderBeek, James C. Gee
• Spatiotemporal Analysis of Structural Changes of the Lamina Cribrosa / Charly Girot, Hiroshi Ishikawa, James Fishbaugh, Gadi Wollstein, Joel Schuman, Guido Gerig
• Fast Blur Detection and Parametric Deconvolution of Retinal Fundus Images / Bryan M. Williams, Baidaa Al-Bander, Harry Pratt, Samuel Lawman, Yitian Zhao, Yalin Zheng et al.
• Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs / Yufan He, Aaron Carass, Yeyi Yun, Can Zhao, Bruno M. Jedynak, Sharon D. Solomon et al.
• Boosted Exudate Segmentation in Retinal Images Using Residual Nets / Samaneh Abbasi-Sureshjani, Behdad Dashtbozorg, Bart M. ter Haar Romeny, François Fleuret
• Development of Clinically Based Corneal Nerves Tortuosity Indexes / Fabio Scarpa, Alfredo Ruggeri
• A Comparative Study Towards the Establishment of an Automatic Retinal Vessel Width Measurement Technique / Fan Huang, Behdad Dashtbozorg, Alexander Ka Shing Yeung, Jiong Zhang, Tos T. J. M. Berendschot, Bart M. ter Haar Romeny
• Automatic Detection of Folds and Wrinkles Due to Swelling of the Optic Disc / Jason Agne, Jui-Kai Wang, Randy H. Kardon, Mona K. Garvin
• Representation Learning for Retinal Vasculature Embeddings / Luca Giancardo, Kirk Roberts, Zhongming Zhao.

This book constitutes the refereed joint proceedings of the International Workshop on Fetal and Infant Image Analysis, FIFI 2017, and the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 8 full papers presented at FIFI 2017 and the 20 full papers presented at OMIA 2017 were carefully reviewed and selected. The FIFI papers feature research on advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period. The OMIA papers cover various topics in the field of ophthalmic image analysis.
(source: Nielsen Book Data)

• Novel imaging and analysis methods for myocardial tissue characterization and remodeling.- Advanced cardiac image analysis tools for diagnostic and interventions.- Electrophysiology: mapping and biophysical modeling.- Biomechanics and flow: modeling and tissue property measurements.
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This book constitutes the refereed proceedings of the 9th International Conference on Functional Imaging and Modeling of the Heart, held in Toronto, ON, Canada, in June 2017. The 48 revised full papers were carefully reviewed and selected from 63 submissions. The focus of the papers is on following topics: novel imaging and analysis methods for myocardial tissue characterization and remodeling; advanced cardiac image analysis tools for diagnostic and interventions; electrophysiology: mapping and biophysical modeling; biomechanics and flow: modeling and tissue property measurements.
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• Intro
• Workshop Editors
• Preface GRAIL 2017
• Organization
• Preface MFCA 2017
• Organization
• Preface MICGen 2017
• Organization
• Contents
• First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017
• Classifying Phenotypes Based on the Community Structure of Human Brain Networks
• 1 Introduction
• 2 Similarity of Brain Network Community Structures
• 2.1 Detecting Communities in Structural Brain Networks
• 2.2 Measuring Distance Between Community Structures
• 3 Classifying Connectomes Based on their Community Structure

• 4 Experiments: Network-Based Alzheimer's Disease Classification
• 4.1 Data and Network Construction
• 4.2 Experimental Setup
• 4.3 Results and Discussion
• 5 Conclusions
• References
• Autism Spectrum Disorder Diagnosis Using Sparse Graph Embedding of Morphological Brain Networks
• 1 Introduction
• 2 Proposed Sparse Graph Embedding of High-Order Morphological Brain Networks for Autism Classification
• 3 Results and Discussion
• 4 Conclusion
• References
• Topology of Surface Displacement Shape Feature in Subcortical Structures
• 1 Introduction
• 2 Methods
• 2.1 Shape Feature

• 2.2 Shape Topology
• 2.3 Persistent Homology
• 2.4 Experiments
• 2.5 Imaging and Demographics
• 3 Results
• 4 Discussion and Conclusion
• References
• Graph Geodesics to Find Progressively Similar Skin Lesion Images
• 1 Introduction
• 2 Methods
• 3 Results
• 4 Conclusions
• References
• Uncertainty Estimation in Vascular Networks
• 1 Introduction
• 2 Background
• 3 Uncertainty Estimation by Means of Sampling
• 3.1 Perturbation Sampler
• 3.2 Gibbs Sampler
• 4 Experiments and Results
• 5 Conclusion
• References

• Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing
• 1 Introduction
• 2 Method
• 2.1 Tracking Individual Branches
• 2.2 Process and Measurement Models
• 2.3 Bayesian Smoothing
• 2.4 Tree as a Collection of Branches
• 2.5 Application to Airways
• 3 Experiments and Results
• 3.1 Data
• 3.2 Error Measure, Initial Parameters and Tuning
• 3.3 Results
• 4 Discussion and Conclusions
• References
• Detection and Localization of Landmarks in the Lower Extremities Using an Automatically Learned Conditional Random Field
• 1 Introduction
• 2 Related Work

• 3 Methods
• 3.1 Landmark Localization Using Regression Tree Ensembles
• 3.2 CRF with Pool of Potential Functions and ``Missing'' Label
• 3.3 Learning of Parameters and Removing Potentials
• 4 Results
• 5 Discussion and Conclusions
• References
• 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017
• Bridge Simulation and Metric Estimation on Landmark Manifolds
• 1 Introduction
• 2 Landmarks Manifolds and Stochastic Landmark Dynamics
• 2.1 Brownian Motion
• 2.2 Large Deformation Stochastics
• 3 Brownian Bridge Simulation
• 3.1 Bridge Sampling

This book constitutes the refereed joint proceedings of the First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, the 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017, and the Third International Workshop on Imaging Genetics, MICGen 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 7 full papers presented at GRAIL 2017, the 10 full papers presented at MFCA 2017, and the 5 full papers presented at MICGen 2017 were carefully reviewed and selected. The GRAIL papers cover a wide range of graph based medical image analysis methods and applications, including probabilistic graphical models, neuroimaging using graph representations, machine learning for diagnosis prediction, and shape modeling. The MFCA papers deal with theoretical developments in non-linear image and surface registration in the context of computational anatomy. The MICGen papers cover topics in the field of medical genetics, computational biology and medical imaging.
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• 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.
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• Analysis on manifolds.- Shape analysis.- Disease diagnosis/progression.- Brain networks an connectivity.- Diffusion imaging.- Quantitative imaging.- Imaging genomics.- Image registration.- Segmentation.- General image analysis. <.
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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.
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• Introduction.- Medical foundations.- Elements of fuzzy set theory.- Cardinalities of interval-valued fuzzy sets and their applications in decision making with imperfect information.- OvaExpert System.- Summary.
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This book discusses computer-supported medical diagnosis with a particular focus on ovarian tumor diagnosis - since ovarian cancer is difficult to diagnose and has high mortality rates, especially in Central and Eastern Europe. It presents the theoretical foundations (both medical and mathematical) of the intelligent OvaExpert system, which supports decision-making in tumor diagnosis. OvaExpert was created primarily to help gynecologists predict the malignancy of ovarian tumors by applying the existing diagnostic models and using modern methods of computational intelligence that accommodate imprecise and imperfect medical data, both of which are common features of everyday medical practice. The book presents novel methods based on interval-valued fuzzy sets and the theory of their cardinalities.
(source: Nielsen Book Data)

This book constitutes the refereed joint proceedings of the 6th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017, and the Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 6 full papers presented at CVII-STENT 2017 and the 11 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)

• 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.
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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.
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• 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. .
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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)

• Fifth International Workshop on Computational Methods for Molecular Imaging, CMMI 2017:
• 3D Lymphoma Segmentation in PET/CT Images Based on Fully Connected CRFs / Yuntao Yu, Pierre Decazes, Isabelle Gardin, Pierre Vera, Su Ruan
• Individual Analysis of Molecular Brain Imaging Data Through Automatic Identification of Abnormality Patterns / Ninon Burgos, Jorge Samper-González, Anne Bertrand, Marie-Odile Habert, Sébastien Ourselin, Stanley Durrleman et al.
• W-Net for Whole-Body Bone Lesion Detection on 68 Ga-Pentixafor PET/CT Imaging of Multiple Myeloma Patients / Lina Xu, Giles Tetteh, Mona Mustafa, Jana Lipkova, Yu Zhao, Marie Bieth et al.
• 3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images / Zisha Zhong, Yusung Kim, John Buatti, Xiaodong Wu
• Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs) / Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng, Michael Fulham
• Second International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017:
• Dynamic Respiratory Motion Estimation Using Patch-Based Kernel-PCA Priors for Lung Cancer Radiotherapy / Tiancheng He, Ramiro Pino, Bin Teh, Stephen Wong, Zhong Xue
• Mass Transportation for Deformable Image Registration with Application to Lung CT / Bartłomiej W. Papież, Sir Michael Brady, Julia A. Schnabel
• Motion-Robust Spatially Constrained Parameter Estimation in Renal Diffusion-Weighted MRI by 3D Motion Tracking and Correction of Sequential Slices / Sila Kurugol, Bahram Marami, Onur Afacan, Simon K. Warfield, Ali Gholipour
• Semi-automatic Cardiac and Respiratory Gated MRI for Cardiac Assessment During Exercise / Bram Ruijsink, Esther Puyol-Antón, Muhammad Usman, Joshua van Amerom, Phuoc Duong, Mari Nieves Velasco Forte et al.
• CoronARe: A Coronary Artery Reconstruction Challenge / Serkan Çimen, Mathias Unberath, Alejandro Frangi, Andreas Maier
• Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks / Yipeng Hu, Eli Gibson, Li-Lin Lee, Weidi Xie, Dean C. Barratt, Tom Vercauteren et al.
• Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging / Steven McDonagh, Benjamin Hou, Amir Alansary, Ozan Oktay, Konstantinos Kamnitsas, Mary Rutherford et al.
• Reconstruction of 3D Cardiac MR Images from 2D Slices Using Directional Total Variation / Nicolas Basty, Darryl McClymont, Irvin Teh, Jürgen E. Schneider, Vicente Grau
• An Efficient Multi-resolution Reconstruction Scheme with Motion Compensation for 5D Free-Breathing Whole-Heart MRI / Rosa-María Menchón-Lara, Javier Royuela-del-Val, Alejandro Godino-Moya, Lucilio Cordero-Grande, Federico Simmross-Wattenberg, Marcos Martín-Fernández et al.
• First International Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017:
• Automated Ventricular System Segmentation in CT Images of Deformed Brains Due to Ischemic and Subarachnoid Hemorrhagic Stroke / E. Ferdian, A. M. Boers, L. F. Beenen, B. M. Cornelissen, I. G. Jansen, K. M. Treurniet et al.
• Towards Automatic Collateral Circulation Score Evaluation in Ischemic Stroke Using Image Decompositions and Support Vector Machines / Yiming Xiao, Ali Alamer, Vladimir Fonov, Benjamin W. Y. Lo, Donatella Tampieri, D. Louis Collins et al.
• The Effect of Non-contrast CT Slice Thickness on Thrombus Density and Perviousness Assessment / M. L. Tolhuisen, J. Enthoven, E. M. M. Santos, W. J. Niessen, L. F. M. Beenen, D. W. J. Dippel et al.
• Quantitative Collateral Grading on CT Angiography in Patients with Acute Ischemic Stroke / Anna M. M. Boers, Renan Sales Barros, Ivo G. H. Jansen, Cornelis H. Slump, Diederik W. J. Dippel, Aad van der Lugt et al.

This book constitutes the refereed joint proceedings of the International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, the International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017, and the International Stroke Workshop: Imaging and Treatment Challenges, SWITCH 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 5 full papers presented at FIFI 2017, the 9 full papers presented at RAMBO 2017, and the 4 full papers presented at SWITCH 2017 were carefully reviewed and selected. The CMMI papers cover various areas from image synthesis to data analysis and from clinical diagnosis to therapy individualization, using molecular imaging modalities PET, SPECT, PET/CT, SPECT/CT, and PET/MR. The RAMBO papers present research from both academia and industry, They are organized into the categories "registration and tracking" and "image reconstruction and information retrieval" while application areas include cardiac, pulmonal, abdominal, fetal, and renal imaging. The SWITCH papers focus on CT(A)-based quantitative imaging biomarkers for stroke.
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• Multi-atlas segmentation.- Segmentation.- Alzheimer's disease.- Reconstruction, denoising, super-resolution.- Tumor, lesion.- Classification, retrival.
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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. The papers are organized in topical sections on multi-atlas segmentation; segmentation; Alzheimer's disease; reconstruction, denoising, super-resolution; tumor, lesion; and classification, retrival.
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• Purpose and Scope of the Monograph.- Introduction.- Image Acquisition.- Image Pre-processing.- Image Processing.- Examples of Tailoring the Algorithm.
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The book presents automatic and reproducible methods for the analysis of medical infrared images. All methods highlighted here have been practically implemented in Matlab, and the source code is presented and discussed in detail. Further, all methods have been verified with medical specialists, making the book an ideal resource for all IT specialists, bioengineers and physicians who wish to broaden their knowledge of tailored methods for medical infrared image analysis and processing.
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• Processing of Hyperspectral Medical Images: Applications in Dermatology Using Matlab (R).- Foreword.- Preface.- Acknowledgments.- List of selected symbols.- Chapte
• r1: Introduction.- Chapte
• r2: Image acquisition.- Chapte
• r3: Image pre-processing.- Chapte
• r4: Image processing.- Chapte
• r5: Classification.- Chapte
• r6: Sensitivity to parameter changes.- Chapte
• r7: Conclusions.- Appendix.- References.- Summary.
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This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods.
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This book contains revised and extended selected papers from two workshops: the 10th International Workshop on Agents Applied in Health Care, A2HC 2017, held at the 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, held in Sao Paulo, Brazil, in May 2017, and the International Workshop on Agents and Multi-Agent Systems for AAL and e-Health, A-HEALTH 2017, held at the 15th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2017, in Porto, Portugal, in June 2017. The 9 revised full papers were carefully reviewed and selected from 16 submissions. They feature current research topics such as personalised health systems for remote and autonomous tele-assistance, communication and co-operation between distributed intelligent agents to manage patient care, information agents that retrieve medical information from distributed repositories, intelligent and distributed data mining, and multi-agent systems that assist the doctors in the tasks of monitoring, decision support and diagnosis. .
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• Invited Talks.- Dice overlap measures for objects of unknown number: Application to lesion segmentation.- Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials.- Brain Lesion Image Analysis.- Automated Segmentation of Multiple Sclerosis Lesions using Multi-Dimensional Gated Recurrent Units.- Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation.- MARCEL (inter-Modality Ane Registration with CorELation ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection.- Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks.- Overall Survival Time Prediction for High Grade Gliomas based on Sparse Representation Framework.- Traumatic Brain Lesion Quantication based on Mean Diusivity Changes.- Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries.- Sub-Acute & Chronic Ischemic Stroke Lesion MRI Segmentation.- Brain Tumor Segmentation Using an Adversarial Network.- Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.- Brain Tumor Image Segmentation.- Deep Learning based Multimodal Brain Tumor Diagnosis.- Multimodal Brain Tumor Segmentation using Ensemble of Forest Method.- Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to brain tumor segmentation.- Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks.- 3D Brain Tumor Segmentation through Integrating Multiple 2D FCNNs.- MRI Brain Tumor Segmentation and Patient Survival Prediction using Random Forests and Fully Convolutional Networks.- Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis.- Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks.- A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor.- Dilated Convolutions for Brain Tumor Segmentation in MRI Scans.- Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation.- TPCNN: Two-phase Patch-based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction.- Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge.- Multi-modal PixelNet for Brain Tumor Segmentation.- Brain Tumor Segmentation using Dense Fully Convolutional Neural Network.- Brain Tumor Segmentation in MRI Scans using Deeply-Supervised Neural Networks.- Brain Tumor Segmentation and Parsing on MRIs using Multiresolution Neural Networks.- Brain Tumor Segmentation using Deep Fully Convolutional Neural Networks.- Glioblastoma and Survival Prediction.- MRI Augmentation via Elastic Registration for Brain Lesions Segmentation.- Cascaded V-Net using ROI masks for brain tumor segmentation.- Brain Tumor Segmentation using a 3D FCN with Multi-Scale Loss.- Brain tumor segmentation using a multi-path CNN based method.- 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences.- Automated Brain Tumor Segmentation on Magnetic Resonance Images (MRIs) and Patient Overall Survival Prediction using Support Vector Machines.- Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation.- Tumor segmentation from multimodal MRI using random forest with superpixel and tensor based feature extraction.- Towards Uncertainty-assisted Brain Tumor Segmentation and Survival Prediction.- Ischemic Stroke Lesion Image Segmentation.- WMH Segmentation Challenge: a Texture-based Classication Approach.- White Matter Hyperintensities Segmentation In a Few Seconds Using Fully Convolutional Network and Transfer Learning.
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This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.
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• Towards Ultra-high Resolution 3D Reconstruction of a Whole Rat Brain from 3D-PLI Data.- FOD-based Registration for Susceptibility Distortion Correction in Connectome Imaging.- GIFE: Efficient and Robust Group-wise Isometric Fiber Embedding.- Multi-Modal Brain Tensor Factorization: Preliminary Results with AD Patients.- Intact Connectional Morphometricity Learning Using Multi-View Morphological Brain Networks with Application to Autism Spectrum Disorder.- Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterm Birth.- Heritability Estimation of Reliable Connectomic Features.- Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains.- Riemannian Regression and Classification Models of Brain Networks Applied to Autism.- Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields.- Data-Specific Feature Selection Method Identification for Most Reproducible Connectomic Feature Discovery Fingerprinting Brain States.- Towards Effective Functional Connectome Fingerprinting.- Connectivity-Driven Brain Parcellation via Consensus Clustering.- GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network Diffusion.- Structural Subnetwork Evolution Across the Lifespan: Rich-club, Feeder, Seeder.
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This book constitutes the refereed proceedings of the Second International Workshop on Connectomics in NeuroImaging, CNI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 15 full papers presented were carefully reviewed and selected from 20 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.
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• Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity.- A Graph Representation and Similarity Measure for Brain Networks with Nodal Features.- Hierarchical Bayesian Networks for Modeling Inter-Class Dependencies: Application to Semi-Supervised Cell Segmentation.- Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion.- BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classification.- Modeling Brain Networks with Artificial Neural Networks.- A Bayesian Disease Progression Model for Clinical Trajectories.- Multi-modal brain connectivity study using deep collaborative learning.- Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum based on Functional Connectomes.- Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging.- Cross-Diagnostic Prediction of Dimensional Psychiatric Phenotypes in Anorexia Nervosa and Body Dysmorphic Disorder Using Multimodal Neuroimaging and Psychometric Data.
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This book constitutes the refereed joint proceedings of the Second International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and the First International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 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 6 full papers presented at GRAIL 2018 and the 5 full papers presented at BeYond MIC 2018 were carefully reviewed and selected. The GRAIL papers cover a wide range of develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. The Beyond MIC papers cover topics of novel methods with significant imaging and non-imaging components, addressing practical applications and new datasets.
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• Medical, health, biomedicine information.- Artificial intelligence for computer-aided diagnosis.- Data management, data mining, and knowledge discovery.- Development of new architectures and applications.
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This book constitutes the refereed proceedings of the 7th International Conference on Health Information Science, HIS 2018, held in Cairns, QLD, Australia, in October 2018. The 13 full papers and 5 short papers presented were carefully reviewed and selected from 43 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 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.
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