Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part IV

Bibliographische Detailangaben

Titel
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part IV
verantwortlich
Martel, Anne L. (HerausgeberIn); Abolmaesumi, Purang (HerausgeberIn); Stoyanov, Danail (HerausgeberIn); Mateus, Diana. (HerausgeberIn); Zuluaga, Maria A. (HerausgeberIn); Zhou, S. Kevin (HerausgeberIn); Racoceanu, Daniel (HerausgeberIn); Joskowicz, Leo. (HerausgeberIn)
Schriftenreihe
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 12264
veröffentlicht
Erscheinungsjahr
2020
Teil von
LNCS sublibrary. ; 12264.
Medientyp
E-Book
Datenquelle
British National Bibliography
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Inhaltsangabe:
  • Segmentation
  • Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
  • DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision
  • KISEG: A Three-Stage Segmentation Framework for Multi-level Acceleration of Chest CT Scans from COVID-19 Patients
  • CircleNet: Anchor-free Glomerulus Detection with Circle Representation
  • Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
  • Diagnostic Assessment of Deep Learning Algorithms for Detection and Segmentation of Lesion in Mammographic images
  • Efficient and Phase-aware Video Super-resolution for Cardiac MRI
  • ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease
  • Deep Generative Model-based Quality Control for Cardiac MRI Segmentation
  • DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation
  • Learning Directional Feature Maps for Cardiac MRI Segmentation
  • Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention
  • XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
  • TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in Ultrasound
  • Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets
  • Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
  • Pay More Attention to Discontinuity for Medical Image Segmentation
  • Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation
  • Deep Class-specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation
  • Memory-efficient Automatic Kidney and Tumor Segmentation Based on Non-local Context Guided 3D U-Net
  • Deep Small Bowel Segmentation with Cylindrical Topological Constraints
  • Learning Sample-adaptive Intensity Lookup Table for Brain Tumor Segmentation
  • Superpixel-Guided Label Softening for Medical Image Segmentation
  • Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation
  • Robust Medical Image Segmentation from Non-expert Annotations with Tri-network
  • Robust Fusion of Probability Maps
  • Calibrated Surrogate Maximization of Dice
  • Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
  • Widening the focus: biomedical image segmentation challenges and the underestimated role of patch sampling and inference strategies
  • Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data
  • Unsupervised Learning for CT Image Segmentation via Adversarial Redrawing
  • Deep Active Contour Network for Medical Image Segmentation
  • Learning Crisp Edge Detector Using Logical Refinement Network
  • Defending Deep Learning-based Biomedical Image Segmentation from Adversarial Attacks: A Low-cost Frequency Refinement Approach
  • CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation
  • KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations
  • LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
  • INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs
  • SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos
  • Orchestrating Medical Image Compression and Remote Segmentation Networks
  • Bounding Maps for Universal Lesion Detection
  • Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks
  • Mt-UcGAN: Multi-task uncertainty-constrained GAN for joint segmentation, quantification and uncertainty estimation of renal tumors on CT
  • Weakly Supervised Deep Learning for Breast Cancer Segmentation with Coarse Annotations
  • Multi-phase and Multi-level Selective Feature Fusion for Automated Pancreas Segmentation from CT Images
  • Asymmetrical Multi-Task Attention U-Net for the Segmentation of Prostate Bed in CT Image
  • Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images
  • Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans
  • Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks
  • E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
  • Universal loss reweighting to balance lesion size inequality in 3D medical image segmentation
  • Brain tumor segmentation with missing modalities via latent multi-source correlation representation
  • Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices
  • Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
  • AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes
  • One Click Lesion RECIST Measurement and Segmentation on CT Scans
  • Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
  • Deep Attentive Panoptic Model for Prostate Cancer Detection Using Biparametric MRI Scans
  • Shape Models and Landmark Detection
  • Graph Reasoning and Shape Constraints for Cardiac Segmentation in Congenital Heart Defect
  • Nonlinear Regression on Manifolds for Shape Analysis using Intrinsic Bézier Splines
  • Self-Supervised Discovery of Anatomical Shape Landmarks
  • Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms
  • Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes
  • Deep Learning Assisted Automatic Intra-operative 3D Aortic Deformation Reconstruction
  • Landmarks Detection with Anatomical Constraints for Total Hip Arthroplasty Preoperative Measurements
  • Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle
  • Miss the point: Targeted adversarial attack on multiple landmark detection
  • Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model
  • Move over there: One-click deformation correction for image fusion during endovascular aortic repair
  • Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model
  • Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels
  • Skip-StyleGAN: Skip-connected Generative Adversarial Networks for Generating 3D Rendered Image of Hand Bone Complex
  • Dynamic multi-object Gaussian process models
  • A kernelized multi-level localization method for flexible shape modeling with few training data
  • Unsupervised Learning and Statistical Shape Modeling of the Morphometry and Hemodynamics of Coarctation of the Aorta
  • Convolutional Bayesian Models for Anatomical Landmarking on Multi-Dimensional Shapes
  • SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation
  • Multi-Task Dynamic Transformer Network for Concurrent Bone Segmentation and Large-Scale Landmark Localization with Dental CBCT
  • Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images using a Local Attention-based Graph Convolution Network.