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
- ; ; ; ; ; ; ;
- 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.