Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2, Lian Chunfeng, Cao Xiaohuan, Rekik Islem
Описание: This book constitutes the proceedings of the Second International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2021, held on September 27, 2021, in conjunction with MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention.
Описание: This book constitutes the proceedings of the International Workshop on Computational Diffusion MRI, CDMRI 2021, which was held on October 1, 2021, in conjunction with MICCAI 2021. The proceedings also contain a paper about the design and scope of the MICCAI Diffusion-Simulated Connectivity Challenge (DiSCo) which was held at CDMRI 2021.
Описание: Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder with Resting-State fMRI.- Error Attention Interactive Segmentation of Medical Images through Matting and Fusion.- A Novel fMRI Representation Learning Framework with GAN.- Semi-supervised Segmentation with Self-Training Based on Quality Estimation and Refinement.- 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies.- Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network.- Self-Recursive Contextual Network for Unsupervised 3D Medical Image Registration.- Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy.- Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows.- Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest.- A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation.- Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network.- Robust Multiple Sclerosis Lesion Inpainting with Edge Prior.- Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation.- GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes.- Anatomy-Aware Cardiac Motion Estimation.- Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation.- LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI.- Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation.- Boundary-aware Network for Kidney Tumor Segmentation.- O-Net: An Overall Convolutional Network for Segmentation Tasks.- Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints.- EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis.- Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation.- Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer.- Exploring Functional Difference between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks.- Detection of Ischemic Infarct Core in Non-Contrast Computed Tomography.- Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers.- Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients.- Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet.- Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification.- Multi-tasking Siamese Networks for Breast Mass Detection using Dual-view Mammogram Matching.- 3D Volume Reconstruction from Single Lateral X-ray Image via Cross-Modal Discrete Embedding Transition.- Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks.- A Deep Network for Joint Registration and Reconstruction of Images with Pathologies.- Learning Conditional Deformable Shape Templates for Brain Anatomy .- Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.- Unsupervised Learning for Spherical Surface Registration.- Anatomy-guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI.- Gyral Growth Patterns of Macaque Brains Revealed by Scattered Orthogonal Nonnegative Matrix Factorization.- Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors.- Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening .- Importance Driven Continual Learning for Segmentation Across Domains.- RDCNet: Instance segmentation with a minimalist recurrent residual network.- Automatic Segmentation of Achilles Tend
Описание: This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021.
Описание: This book constitutes the refereed proceedings of the 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.*The 14 full papers presented were carefully reviewed and selected from 18 submissions.
Описание: Adjacent Scale Fusion and Corneal Position Embedding for Corneal Ulcer Segmentation.- Longitudinal detection of diabetic retinopathy early severity grade changes using deep learning.- Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach using High Quality iOCT 3D Scans.- Diabetic Retinopathy Detection based on Weakly Supervised Object Localization and Knowledge Driven Attribute Mining.- FARGO: A Joint Framework for FAZ and RV Segmentation from OCTA Images.- CDLRS: Collaborative Deep Learning Model with Joint Regression and Segmentation for Automatic Fovea Localization.- U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina.- Radial U-Net: Improving DMEK Graft Detachment Segmentation in Radial AS-OCT Scans.- Guided Adversarial Adaptation Network for Retinal and Choroidal Layer Segmentation.- Juvenile Refractive Power Prediction based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network.- Peripapillary Atrophy Segmentation with Boundary Guidance.- Are cardiovascular risk scores from genome and retinal image complementary? A deep learning investigation in a diabetic cohort.- Dual-branch Attention Network and Atrous Spatial Pyramid Pooling for Diabetic Retinopathy Classification Using Ultra-Widefield Images.- Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images.- Multi-Modality Images Analysis: A Baseline for Glaucoma Grading via Deep Learning.- Impact of data augmentation on retinal OCT image segmentation for diabetic macular edema analysis.- Representation and Reconstruction of Image-Based Structural Patterns of Glaucomatous Defects Using Only Two Latent Variables from a Variational Autoencoder.- Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification.- Attention Guided Slit Lamp Image Quality Assessment.- Robust Retinal Vessel Segmentation from a Data Augmentation Perspective.
Описание: This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.
Описание: This book constitutes the refereed proceedings of the 5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The 19 full papers presented were carefully reviewed and selected from 27 submissions.
Описание: This book constitutes the proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assistend Intervention, MICCAI 2020, in October 2020.
Описание: This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions.
Описание: This book constitutes the refereed proceedings of the First International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 20 full papers presented were carefully reviewed and selected from 24 submissions.
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