Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Springer International Publishing (Verlag)
978-3-030-59718-4 (ISBN)
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
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 Atte
| Erscheinungsdatum | 04.10.2020 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XXXVII, 831 p. 22 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1312 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Applications • Artificial Intelligence • Bioinformatics • Computer Aided Diagnosis • Computer Science • computer vision • conference proceedings • Image Analysis • Image Processing • image reconstruction • Image Segmentation • Imaging Systems • Informatics • machine learning • Medical Image Analysis • Medical Images • Neural networks • Object recognition • pattern recognition • Research • segmentation methods • Signal Processing |
| ISBN-10 | 3-030-59718-0 / 3030597180 |
| ISBN-13 | 978-3-030-59718-4 / 9783030597184 |
| Zustand | Neuware |
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