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• Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging.- Multi-channel Residual Neural Network Based on Squeeze-and Excitation for Osteoporosis Diagnosis.- Machine Learning Based Approach for Motion Detection andEstimation in Routinely Acquired Low Resolution Near Infrared Fluorescence Optical Imaging.- Automatic Landmark Identification on IntraOralScans.- STAU-Net: A Spatial Structure Attention Network for 3D Coronary Artery Segmentation.- Convolutional Redistribution Network for Multi-View Medical Image Diagnosis.- Feature Patch Based Attention Model for Dental Caries Classification.- Conditional Domain Adaptation Based on Initial Distribution Discrepancy for EEG Emotion Recognition.- Automated Cone and Vessel Analysis in Adaptive Optics like RetinalImages for Clinical Diagnostics Support.
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This book constitutes the proceedings of the 11th Workshop on Clinical Image-Based Procedures, CLIP 2022, which was held in conjunction with MICCAI 2022, in Singapore in September 2022. The 9 full papers included in this book were carefully reviewed and selected from 12 submissions. They focus on the applicability of basic research methods in the clinical practice by creating holistic patient models as an important step towards personalized healthcare. .
(source: Nielsen Book Data)

• Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification.- DFUC2022 Challenge Papers.- HarDNet-DFUS: Enhancing Backbone and Decoder of HarDNet-MSEGfor Diabetic Foot Ulcer Image Segmentation.- OCRNet For Diabetic Foot Ulcer Segmentation Combined with Edge Loss 30.- On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness.- Capture the Devil in the Details via Partition-then-Ensemble on Higher Resolution Images.- Unconditionally Generated and Pseudo-Labeled Synthetic Images for Diabetic Foot Ulcer Segmentation Dataset Extension.-Post Challenge Paper.- Diabetic Foot Ulcer Segmentation Using Convolutional and Transformer-based Refined Mixup Augmentation for Diabetic Foot Ulcer Segmentation.- Organization IX DFU-Ens: End-to-End Diabetic Foot Ulcer Segmentation Framework with Vision Transformer Based Detection.- Summary Paper.- Diabetic Foot Ulcer Grand Challenge 2022 Summary.
• (source: Nielsen Book Data)

This book constitutes the Third Diabetic Foot Ulcers Grand Challenge, DFUC 2022, which was held on September 2022, in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 in Singapore. The 8 full papers presented together with 5 challenge papers and 3 post-challenge papers included in this book were carefully reviewed and selected from 19 submissions.The DFU challenges aim to motivate the health care domain to share datasets, participate in ground truth annotation, and enable data-innovation in computer algorithm development. In the longer term, it will lead to improved patient care.
(source: Nielsen Book Data)

• Cardiac multiscale structure
• Cardiac electrophysiology modeling
• Image and shape analysis
• Cardiovascular hemodynamics and CFD
• Cardiac biomechanics
• Clinical applications

This book constitutes the refereed proceedings of the 12th International Conference on Functional Imaging and Modeling of the Heart, held in Lyon, France, in June 2023. The 72 full papers were carefully reviewed and selected from 80 submissions. The focus of the papers is on following topics: increased imaging resolutions, data explosion, sophistication of computational models and advent of AI frameworks, while new imaging modalities have emerged (e.g. combined PET-MRI, Spectral CT).

• Applying Disentanglement in the Medical Domain: An Introduction.- HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information.- Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs.- Disentangled Representation Learning for Privacy-Preserving Case-Based Explanations.- Instance-Specific Augmentation of Brain MRIs with Variational Autoencoder.- Low-rank and Sparse Metamorphic Autoencoders for Unsupervised Pathology Disentanglement.- Training -VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder.- Disentangling Factors of Morpholigical Variation in an Invertible Brain Aging Model.- A study of representational properties of unsupervised anomaly detection in brain MRI.
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This book constitutes the post-conference proceedings of the First MICCAI Workshop on Medical Applications with Disentanglements, MAD 2022, held in conjunction with MICCAI 2022, in Singapore, on September22, 2022. The 8 full papers presented in this book together with one short paper were carefully reviewed and cover generative adversarial networks (GAN), variational autoencoders (VAE) and normalizing-flow architectures as well as a wide range of medical applications, like brain age prediction, skull reconstruction and unsupervised pathology disentanglement.
(source: Nielsen Book Data)

• Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data.- Learning correspondences of cardiac motion using biomechanics-informed modeling.- Multi-modal Latent-space Self-alignment for Super-resolution Cardiac MR Segmentation.- Towards real-time optimization of left atrial appendage occlusion device placement through physics-informed neural networks.- Haemodynamic changes in the fetal circulation after connection to an artificial placenta: a computational modelling study.- Personalized Fast Electrophysiology Simulations to Evaluate Arrhythmogenicity of Ventricular Slow Conduction Channels.- Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations.- Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling.- Comparison of Semi- and Un-supervised Domain Adaptation Methods for Whole-Heart Segmentation.- Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging.- An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallot.- Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal.- Improving Echocardiography Segmentation by Polar Transformation.- Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach.- Interpretable Prediction of Post-Infarct Ventricular Arrhythmia using Graph Convolutional Network.- Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers.- Sensitivity analysis of left atrial wall modeling approaches and inlet/outlet boundary conditions in fluid simulations to predict thrombus formation.- APHYN-EP: Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics.- Unsupervised machine-learning exploration of morphological and haemodynamic indices to predict thrombus formation at the left atrial appendage.- Geometrical deep learning for the estimation of residence time in the left atria.- Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection.- A systematic study of race and sex bias in CNN-based cardiac MR segmentation.- Mesh U-Nets for 3D Cardiac Deformation Modeling.- Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.- Simplifying Disease Staging Models into a Single Anatomical Axis - A Case Study of Aortic Coarctation In-utero.- Point2Mesh-Net: Combining Point Cloud and Mesh-Based Deep Learning for Cardiac Shape Reconstruction.- Post-Infarction Risk Prediction with Mesh Classification Networks.- Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries.- Computerized Analysis of the Human Heart to Guide Targeted Treatment of Atrial Fibrillation.- 3D Mitral Valve Surface Reconstruction from 3D TEE via Graph Neural Networks.- Efficient MRI Reconstruction with Reinforcement Learning for Automatic Acquisition Stopping.- Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels.- PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images.- Deep Computational Model for the Inference of Ventricular Activation Properties.- Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels.- Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts.- Deep Learning Based Classification and Segmentation for Cardiac Magnetic Resonance Imaging with Respiratory Motion Artifacts.- Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation.- Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts using Multi-task Learning and K-Space Motion Artefact Augmentation.- Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI.- Automatic Image Quality Assessment and Cardiac Segmentation Based on CMR Images.- Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation.- 3D MRI cardiac segmentation under respiratory motion artifacts.- Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifact using Simulated Data.- Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification.- Automatic Cardiac Magnetic Resonance Respiratory Motions Assessment and Segmentation.- Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation.- A deep learning-based fully automatic framework for motion-existing cine image quality control and quantitative analysis.
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This book constitutes the proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th MICCAI conference. The 34 regular workshop papers included in this volume were carefully reviewed and selected after being revised and deal with topics such as: common cardiac segmentation and modelling problems to more advanced generative modelling for ageing hearts, learning cardiac motion using biomechanical networks, physics-informed neural networks for left atrial appendage occlusion, biventricular mechanics for Tetralogy of Fallot, ventricular arrhythmia prediction by using graph convolutional network, and deeper analysis of racial and sex biases from machine learning-based cardiac segmentation. In addition, 14 papers from the CMRxMotion challenge are included in the proceedings which aim to assess the effects of respiratory motion on cardiac MRI (CMR) imaging quality and examine the robustness of segmentation models in face of respiratory motion artefacts. A total of 48 submissions to the workshop was received.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the first International Workshop on Applications of Medical Artificial Intelligence, AMAI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022. The book includes 17 papers which were carefully reviewed and selected from 26 full-length submissions. Practical applications of medical AI bring in new challenges and opportunities. The AMAI workshop aims to engage medical AI practitioners and bring more application flavor in clinical, evaluation, human-AI collaboration, new technical strategy, trustfulness, etc., to augment the research and development on the application aspects of medical AI, on top of pure technical research.
(source: Nielsen Book Data)

The two volume set LNCS 13258 and 13259 constitutes the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, held in Puerto de la Cruz, Tenerife, Spain in May June 2022. The total of 121 contributions was carefully reviewed and selected from 203 submissions. The papers are organized in two volumes, with the following topical sub-headings: Part I: Machine Learning in Neuroscience; Neuromotor and Cognitive Disorders; Affective Analysis; Health Applications, Part II: Affective Computing in Ambient Intelligence; Bioinspired Computing Approaches; Machine Learning in Computer Vision and Robot; Deep Learning; Artificial Intelligence Applications.

• Preface MIDOG 2021.- Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmainGeneralization Challenge.- Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images.- Domain-Robust Mitotic Figure Detection with StyleGAN.- Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images.- Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation.- Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge.- MitoDet: Simple and robust mitosis detection.- Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection.- Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge.- Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classi cation Model for MIDOG Challenge.- Domain Adaptive Cascade R-CNN for Mitosis DOmain Generalization (MIDOG) Challenge.- Reducing Domain Shift For Mitosis Detection Using Preprocessing Homogenizers.- Cascade RCNN for MIDOG Challenge.- Sk-Unet Model with Fourier Domain for Mitosis Detection.- Preface MOOD21.- Self-Supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation.- Self-Supervised Medical Out-of-Distribution Using U-Net Vision Transformers.- SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes.- MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision.- AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation.- Preface Learn2Reg 2021.- Deformable Registration of Brain MR Images via a Hybrid Loss.- Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge.- Unsupervised Volumetric Displacement Fields Using Cost Function Unrolling.- Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge.- The Learn2Reg 2021 MICCAI Grand Challenge (PIMed Team).- Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021.- Progressive and Coarse-to-fine Network for Medical Image Registration across Phases, Modalities and Patients. -Semi-supervised Multilevel Symmetric Image Registration Method for Magnetic Resonance Whole Brain Images.
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This book constitutes three challenges that were held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, which was planned to take place in Strasbourg, France but changed to an online event due to the COVID-19 pandemic. The peer-reviewed 18 long and 9 short papers included in this volume stem from the following three biomedical image analysis challenges: Mitosis Domain Generalization Challenge (MIDOG 2021), Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and Learn2Reg (L2R 2021). The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.
(source: Nielsen Book Data)

• Supervoxel Merging towards Brain Tumor Segmentation.- Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI.- Modeling multi-annotator uncertainty as multi-class segmentation problem.- Modeling multi-annotator uncertainty as multi-class segmentation problem.- Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma.- Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks.- Optimization of Deep Learning based Brain Extraction in MRI for Low Resource Environments. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task.- Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation.- BRAT
• S2021: exploring each sequence in multi-modal input for baseline U-net performance.- Automatic Brain Tumor Segmentation using Multi-scale Features and Attention Mechanism.- Simple and Fast Convolutional Neural Network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans.- MSViT: Multi Scale Vision Transformer forBiomedical Image Segmentation.- Unsupervised Multimodal.- HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation.- Multimodal Brain Tumor Segmentation Algorithm.- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.- Multi-plane UNet++ Ensemble for Glioblastoma Segmentation.- Multimodal Brain Tumor Segmentation using Modified UNet Architecture.- A video data based transfer learning approach for classification of MGMT status in brain tumor MR images.- Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021.- 3D MRI brain tumour segmentation with autoencoder regularization and Hausdorff distance loss function.- 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge.- Cascaded training pipeline for 3D brain tumor segmentation.- nnU-Net with Region-based Training and Loss Ensembles for Brain Tumor Segmentation.- Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining.- Automatic segmentation of brain tumor using 3D convolutional neural networks.- Hierarchical and Global Modality Interaction for Brain Tumor Segmentation.- Ensemble Outperforms Single Models in Brain Tumor Segmentation.- Brain Tumor Segmentation using UNet-Context Encoding Network.- Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI.
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This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book.
(source: Nielsen Book Data)

• BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.- Optimized U-Net for Brain Tumor Segmentation.- MS UNet: Multi-Scale 3D UNet for Brain Tumor Segmentation.- Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database.- Orthogonal-Nets: A large ensemble of 2D neural networks for 3D Brain Tumor Segmentation.- Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation.- MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks.- Brain Tumor Segmentation with Patch-based 3D Attention UNet from Multi-parametric MRI.- Dice Focal Loss with ResNet-like Encoder-Decoder architecture in 3D Brain Tumor Segmentation.- HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging using Model Ensembling and Super-resolution.- Quality-aware Model Ensemble for Brain Tumor Segmentation.- Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs.- An Ensemble Approach to Automatic Brain Tumor Segmentation.- Extending nn-UNet for brain tumor segmentation.- Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge.- Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI.- Deep Learning based Ensemble Approach for 3D MRI Brain Tumor Segmentation.- Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features.- bining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation.- Automatic Brain Tumor Segmentation with a Bridge-Unet deeply supervised enhanced with downsampling pooling combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm.
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This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually.
(source: Nielsen Book Data)

• Distributed, Collaborative, and Federated Learning.- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
• FedAP: Adaptive Personalization in Federated Learning for Non-IID Data Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?
• Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?.- Can collaborative learning be private, robust and scalable?.- Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation.- Joint Multi Organ and Tumor Segmentation from Partial Labels using Federated Learning.- Fuh, Kensaku Mori, Weichung Wang, Holger R Roth GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging.- A Specificity-Preserving Generative Model for Federated MRI Translation.- Content-Aware Differential Privacy with Conditional Invertible Neural Networks.- DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain.- Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images.- Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling.- Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana.- Towards Sparsified Federated Neuroimaging Models via Weight Pruning.- Affordable AI and Healthcare.- Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection.- Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions.- Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks.- LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network.
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This book constitutes the refereed proceedings of the Third MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the Second MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. FAIR 2022 was held as a hybrid event.DeCaF 2022 accepted 14 papers from the 18 submissions received. The workshop aims at creating a scientific discussion focusing on the comparison, evaluation, and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases or where information privacy is a priority. For FAIR 2022, 4 papers from 9 submissions were accepted for publication. The topics of the accepted submissions focus on deep ultrasound segmentation, portable OCT image quality enhancement, self-attention deep networks and knowledge distillation in low-regime setting.
(source: Nielsen Book Data)

• Applications of Health and Medical Data.- Evidence extraction to validate medical claims in fake news detection.- Detection of obsessive-compulsive disorder in Australian children and adolescents using machine learning methods.- An Anomaly Detection Framework Based on Data Lake for Medical Multivariate Time Series.- Anomaly Detection on Health Data.- DRAM-Net: A Deep Residual Alzheimer's Diseases and Mild Cognitive Impairment Detection Network Using EEG Data.- An Intelligence Model for Blood Pressure Estimation from Photoplethysmography Signal.- Tailored Nutrition Service to Reduce the Risk of Chronic Diseases.- Combining Process Mining And Time Series Forecasting To Predict Hospital Bed Occupancy.- HGCL: Heterogeneous Graph Contrastive Learning for Traditional Chinese Medicine Prescription Generation.- Fractional Fourier Transform Aided Computerized Framework for Alcoholism Identification in EEG.- Learning optimal treatment strategies for sepsis using online reinforcement learning in continuous space.- Health and Medical Data Processing.- MHDML:Construction of A Medical Lakehouse for Multi-source Heterogeneous Data.- Data Exploration Optimization for Medical Big Data.- Improving Data Analytic Performance in Health Information System with Big Data Technology.- HoloCleanX: A Multi-source Heterogeneous Data Cleaning Solution Based on Lakehouse Platform.- The construction and validation of an automatic crisis balance analysis model.- Assessing the Utilization of TELedentistry from perspectives of earlycareer dental practitioners - development of the UTEL Questionnaire.- Genetic Algorithm for Patient Assignment Optimization in Cloud Healthcare System.- Research on the Crisis Intervention Strategy Service System.- Towards a Perspective to Analyze Emergent Sytems in the Health Domain.- Health and Medical Data Mining via Graph-based Approaches.- Food recommendation for mental health by using knowledge graph approach.- Medical Knowledge Graph Construction Based on Traceable Conversion.- Medical Knowledge Graph Construction Based on Traceable Conversion.- Alcoholic EEG Data Classification Using Weighted Graph Based Technique.- Health and Medical Data Classification.- Optical Coherence Tomography Classification based on Transfer Learning and RA-Attention.- Intelligent Interpretation and Classification of Multivariate Medical time series based on Convolutional Neural Networks.- ECG Signals Classification Model Based on Frequency domain Features Coupled with Least Square Support Vector Machine (LS-SVM).- Cluster analysis of low-dimensional medical concept representations from Electronic Health Records.
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This book constitutes the refereed proceedings of the 11th International Conference on Health Information Science, HIS 2022, held in Virtual Event during October 28-30, 2022. The 20 full papers and 9 short papers included in this book were carefully reviewed andselected from 54 submissions. They were organized in topical sections as follows: applications of health and medical data; health and medical data processing; health and medical data mining via graph-based approaches; and health and medical data classification.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 26th Conference on Medical Image Understanding and Analysis, MIUA 2022, held in Cambridge, UK, in July 2022. The 65 full papers presented were carefully reviewed and selected from 95 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging. Chapter "FCN-Transformer Feature Fusion for Polyp Segmentation" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
(source: Nielsen Book Data)

• M^2F: Multi-modal and Multi-task Fusion Network for Glioma Diagnosis and Prognosis.- Visual Modalities based Multimodal Fusion for Surgical Phase Recognition.- Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images.- Vessel Segmentation via Link Prediction of Graph Neural Networks.- A Bagging Strategy-Based Multi-Scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset.- Liver Segmentation Quality Control in Multi-Sequence MR Studies.- Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI.- Gabor Filter-Embedded U-Net with Transformer-based Encoding for Biomedical Image Segmentation.- Learning-based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study.- Coordinate Translator for Learning Deformable Medical Image Registration.- Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation.- Improve Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT scans.
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This book constitutes the refereed proceedings of the Third International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with MICCAI 2022 in singapore, in September 2022.The 12 papers presented were carefully reviewed and selected from 18 submissions. The MMMI workshop aims to advance the state of the art in multi-scale multi-modal medical imaging, including algorithm development, implementation of methodology, and experimental studies. The papers focus on medical image analysis and machine learning, especially on machine learning methods for data fusion and multi-score learning.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• AugPaste: One-Shot Anomaly Detection for Medical Images
• 1 Introduction
• 2 Methods
• 2.1 Construction of Lesion Bank
• 2.2 Synthesis of Anomalous Samples
• 2.3 Anomaly Detection Network
• 2.4 Implementation Details
• 3 Experiments and Results
• 3.1 Datasets
• 3.2 Evaluation Metric
• 3.3 Ablation Studies on EyeQ
• 3.4 Comparison with State-of-the-Art
• 4 Conclusion
• References
• Analysing Optical Coherence Tomography Angiography of Mid-Life Persons at Risk of Developing Alzheimer's Disease Later in Life
• 1 Introduction

• 2 Methodology
• 3 Results
• 3.1 Vessel Tortuosity Decreases in Risk Groups
• 3.2 Longitudinal Variations of Retinal Features in Risk Groups
• 4 Discussion
• 5 Conclusion
• References
• Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images
• 1 Introduction
• 2 Related Work
• 3 Methodology
• 3.1 Robust Feature Learning Architecture
• 3.2 Proposed Representation Learning Loss
• 3.3 Final Objective Function
• 4 Experiments
• 4.1 Data-Set Processing
• 4.2 Hyper-parameter Tuning
• 4.3 Performance Metrics

• 4.4 Quantitative Evaluation
• 4.5 Qualitative Evaluation
• 5 Conclusion and Future Work
• References
• GUNet: A GCN-CNN Hybrid Model for Retinal Vessel Segmentation by Learning Graphical Structures
• 1 Introduction
• 2 Method
• 2.1 GUNet
• 2.2 Graph Convolution
• 2.3 Graph Construction
• 3 Experiments
• 3.1 Datasets and Evaluation Metrics
• 3.2 Implementation Details
• 4 Results
• 4.1 Experiments on Fundus Photography
• 4.2 Experiments on SLO Images
• 4.3 Visualization
• 5 Conclusion
• References
• Detection of Diabetic Retinopathy Using Longitudinal
• 1 Introduction

• 2 Methods
• 2.1 Longitudinal Siamese
• 2.2 Longitudinal Self-supervised Learning
• 2.3 Longitudinal Neighbourhood Embedding
• 3 Dataset
• 4 Experiments and Results
• 4.1 Comparison of the Approaches on the Early Change Detection
• 4.2 Norm of Trajectory Vector Analyze
• 5 Discussion
• References
• Multimodal Information Fusion for Glaucoma and Diabetic Retinopathy Classification
• 1 Introduction
• 2 Methods
• 2.1 Early Fusion
• 2.2 Intermediate Fusion
• 2.3 Hierarchical Fusion
• 3 Material and Experiments
• 3.1 Data
• 3.2 Data Pre-processing
• 3.3 Implementation Details

• 4 Results
• 4.1 GAMMA Dataset
• 4.2 PlexEliteDR Dataset
• 5 Conclusion
• References
• Mapping the Ocular Surface from Monocular Videos with an Application to Dry Eye Disease Grading
• 1 Introduction
• 2 Proposed Method
• 3 Experiments and Results
• 3.1 SiGMoid
• 3.2 DED Diagnosis: Classification
• 4 Discussion and Conclusion
• References
• Rethinking Retinal Image Quality: Treating Quality Threshold as a Tunable Hyperparameter
• 1 Introduction
• 2 Methods
• 2.1 Quality Prediction on a Categorical Scale and Continuous Scale
• 2.2 Effect of Varying Image Quality Threshold

This book constitutes the refereed proceedings of the 9th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2022. The 20 papers presented at OMIA 2022 were carefully reviewed and selected from 33 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies.
(source: Nielsen Book Data)

• Federated Time-dependent GNN Learning from Brain Connectivity Data with Missing Timepoints.- Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing.- Multi-Tracer PET Imaging Using Deep Learning: Applications in Patients with High-Grade Gliomas.- Multiple Instance Neuroimage Transformer.- Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach.- Mixup augmentation improves age prediction from T1-weighted brain MRI scans.- Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning.- MISS-Net: Multi-view contrastive transformer network for MCI stages prediction using brain 18F-FDG PET imaging.- TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation.- Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study.- Weakly-Supervised TILs Segmentation based on Point Annotations using Transfer Learning with Point Detector and Projected-Boundary Regressor.- Discriminative Deep Neural Network for Predicting Knee OsteoArthritis in Early Stage.- Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-Task Learning on Imaging and Tabular Data.- Quantifying the Predictive Uncertainty of Regression GNN Models Under Target Domain Shifts.- Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets.- Learning subject-specific functional parcellations from cortical surface measures.- A Triplet Contrast Learning of Global and Local Representations for Unannotated Medical Images.- Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification.- Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning.
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This book constitutes the proceedings of the 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with MICCAI 2022 as a hybrid event in Singapore, in September 2022.The 19 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
• 1 Introduction
• 1.1 Related Works
• 1.2 Contributions
• 2 Methods
• 2.1 Generators
• 2.2 Discriminators
• 2.3 Losses
• 3 Experiments
• 3.1 Evaluation
• 3.2 Implementation
• 3.3 Data
• 3.4 Results
• 4 Conclusion
• References
• Generating Artificial Artifacts for Motion Artifact Detection in Chest CT
• 1 Introduction
• 2 Methods
• 3 Experiments
• 4 Results
• 5 Discussion
• References

• Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data
• 1 Introduction
• 2 Probabilistic Image Diversification
• 3 Experiments and Results
• 3.1 Data Augmentation
• 3.2 Benchmarking
• 3.3 Test-Time Augmentation
• 4 Discussion and Conclusion
• References
• Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs
• 1 Introduction
• 1.1 Contributions
• 2 Method
• 2.1 Pathology Synthesis
• 2.2 Modeling Slice Relationship
• 2.3 Data and Implementation
• 3 Results
• 3.1 Pathology Synthesis
• 3.2 Modeling the Slice Relationship

• 4 Discussion and Conclusion
• References
• .26em plus .1em minus .1emHealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease
• 1 Introduction
• 2 HealthyGAN: The Proposed Method
• 2.1 Network Architecture
• 2.2 Training
• 2.3 Detecting Anomalies
• 3 Experiments and Results
• 3.1 COVID-19 Detection
• 3.2 Chest X-ray 14 Diseases Detection
• 3.3 Migraine Detection
• 4 Conclusion
• A Implementation Details
• B Network Architectures
• B.1 Discriminator
• B.2 Generator
• References

• Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement
• 1 Introduction
• 2 Methodology
• 3 Experiments and Results
• 4 Conclusion
• References
• Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain
• 1 Introduction
• 2 Background
• 2.1 VQ-VAE
• 2.2 Transformer
• 3 Methods
• 3.1 Descriptive Quantization for Transformer Usage
• 3.2 Autoregressive Modelling of the Brain
• 4 Experiments and Results
• 4.1 Quantitative Image Fidelity Evaluation
• 4.2 Morphological Evaluation
• 5 Conclusion
• 6 Appendix
• 6.1 VQ-VAEs

• 6.2 Transformers
• 6.3 Losses
• 6.4 Datasets
• 6.5 VBM Analysis
• References
• Can Segmentation Models Be Trained with Fully Synthetically Generated Data?
• 1 Background
• 2 Materials and Methods
• 2.1 Materials
• 2.2 Methods
• 2.3 Segmentation Network Used for the Experiments
• 3 Experiments
• 3.1 Can We Learn to Segment Healthy Regions Using Synthetic Data?
• 3.2 Can Synthetic Generative Models Address Out-of-Distribution Segmentation?
• 3.3 Can We Learn to Segment Pathologies from Synthetic Data?
• 4 Discussion and Conclusion
• A Training Set-Ups

This book constitutes the refereed proceedings of the 7th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2022, held in conjunction with MICCAI 2022, in Singapore, Singapore in September 2022.
(source: Nielsen Book Data)

• Computer Aided Identification of Motion Disturbances Related to Parkinson's Disease
• Prediction of Severity and Treatment Outcome for ASD from fMRI
• Enhancement of Perivascular Spaces Using a Very Deep 3D Dense Network
• Generation of Amyloid PET Images via Conditional Adversarial Training for Predicting Progression to Alzheimer's Disease
• Prediction of Hearing Loss Based on Auditory Perception: A Preliminary Study
• Predictive Patient Care: Survival Model to Prevent Medication Non-adherence
• Joint Robust Imputation and Classification for Early Dementia Detection Using Incomplete Multi-Modality Data
• Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease
• Predicting Nucleus Basalis of Meynert Volume from Compartmental Brain Segmentations
• Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis
• Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI
• Multi-View Brain Network Prediction From a Source View Using Sample Selection via CCA-based Multi-Kernel Connectomic Manifold Learning
• Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes
• Predictive Modeling of Longitudinal Data for Alzheimer's Disease Diagnosis Using RNNs
• Towards Continuous Health Diagnosis from Faces with Deep Learning
• XmoNet: A Fully Convolutional Network for Cross-Modality MR Image Inference
• 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer's Disease Diagnosis
• Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI
• Diffusion MRI Spatial Super-Resolution Using Generative Adversarialv Networks
• Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks.

This book constitutes the refereed proceedings of the First International Workshop on PRedictive Intelligence in MEdicine, PRIME 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 20 full papers presented were carefully reviewed and selected from 23 submissions. The main aim of the workshop is to propel the advent of predictive models in a broad sense, with application to medical data. Particularly, the workshop will admit papers describing new cutting-edge predictive models and methods that solve challenging problems in the medical field.

• Workshop Overview
• Overview of the Third Workshop on Medical Content-Based Retrieval for Clinical Decision Support (MCBR-CDS 2012) / Henning Müller, Hayit Greenspan
• Invited Talk
• A Polynomial Model of Surgical Gestures for Real-Time Retrieval of Surgery Videos / Gwénolé Quellec, Mathieu Lamard, Zakarya Droueche, Béatrice Cochener, Christian Roux
• Methods
• Exploiting 3D Part-Based Analysis, Description and Indexing to Support Medical Applications / Chiara Eva Catalano, Francesco Robbiano, Patrizia Parascandolo, Lorenzo Cesario
• Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models / Shulin Yang, Linda Shapiro, Michael Cunningham, Matthew Speltz, Craig Birgfeld
• 3D/4D Data Retrieval
• Retrieval of 4D Dual Energy CT for Pulmonary Embolism Diagnosis / Antonio Foncubierta-Rodríguez, Alejandro Vargas, Alexandra Platon
• Immediate ROI Search for 3-D Medical Images / Karen Simonyan, Marc Modat, Sebastien Ourselin, David Cash, Antonio Criminisi
• The Synergy of 3D SIFT and Sparse Codes for Classification of Viewpoints from Echocardiogram Videos / Yu Qian, Lianyi Wang, Chunyan Wang, Xiaohong Gao
• Assessing the Classification of Liver Focal Lesions by Using Multi-phase Computer Tomography Scans / Auréline Quatrehomme, Ingrid Millet, Denis Hoa, Gérard Subsol, William Puech
• Invited Talk
• VISCERAL: Towards Large Data in Medical Imaging -- Challenges and Directions / Georg Langs, Allan Hanbury, Bjoern Menze, Henning Müller
• Visual Features
• Customised Frequency Pre-filtering in a Local Binary Pattern-Based Classification of Gastrointestinal Images / Sebastian Hegenbart, Stefan Maimone, Andreas Uhl, Andreas Vécsei, Georg Wimmer
• Bag-of-Colors for Biomedical Document Image Classification / Alba García Seco de Herrera, Dimitrios Markonis, Henning Müller
• Multimodal Retrieval
• An SVD-Bypass Latent Semantic Analysis for Image Retrieval / Spyridon Stathopoulos, Theodore Kalamboukis
• Multimedia Retrieval in a Medical Image Collection: Results Using Modality Classes / Angel Castellanos, Xaro Benavent, Ana García-Serrano, J. Cigarrán.

This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2012, held in Nice, France, in October 2012. The 10 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 15 submissions. The papers are divided on several topics on image analysis of visual or multimodal medical data (X-ray, MRI, CT, echo videos, time series data), machine learning of disease correlations in visual or multimodal data, algorithms for indexing and retrieval of data from visual or multimodal medical databases, disease model-building and clinical decision support systems based on visual or multimodal analysis, algorithms for medical image retrieval or classification, systems of retrieval or classification using the ImageCLEF collection.
(source: Nielsen Book Data)

• Shape, geometry and registration
• Markov models for image reconstruction and analysis
• Automatic anatomy localization via classification
• Texture analysis
• Segmentation.

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2010, held in Beijing, China, in September 2010 as a satellite event of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2010. The 10 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 38 initial submissions. The papers explore the use of modern image recognition technology in tasks such as semantic anatomy parsing, automatic segmentation and quantification, anomaly detection and categorization, data harvesting, semantic navigation and visualization, data organization and clustering, and general-purpose automatic understanding of medical images.
(source: Nielsen Book Data)