Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Springer International Publishing (Verlag)
978-3-030-59724-5 (ISBN)
- Titel ist leider vergriffen;
keine Neuauflage - Artikel merken
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.
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
Angiography and Vessel Analysis.- Lightweight Double Attention-fused Networks for Intraoperative Stent Segmentation.- TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling.- Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction.- Branch-aware Double DQN for Centerline Extraction in Coronary CT Angiography.- Automatic CAD-RADS Scoring from CCTA Scans using Deep Learning.- Higher-Order Flux with Spherical Harmonics Transform for Vascular Analysis.- Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network.- Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement.- Time matters: Handling spatio-temporal perfusion information for automated TICI scoring.- ID-Fit: Intra-saccular Device adjustment for personalized cerebral aneurysm treatment.- JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation.- Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images.- Vascular surface segmentation for intracranial aneurysm isolation and quantification.- Breast Imaging.- Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities.- 2D X-ray mammography and 3D breast MRI registration.- A Second-order Subregion Pooling Network for Breast Ultrasound Lesion Segmentation.- Multi-Scale Gradational-Order Fusion Framework for Breast lesions Classification Using Ultrasound images.- Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network.- Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound.- MommiNet: Mammographic Multi-View Mass Identification Networks.- Multi-Site Evaluation of a Study-Level Classifier for Mammography using Deep Learning.- The case of missed cancers: Applying AI as a radiologist's safety net.- Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models.- Multi-task learning for detection and classification of cancer in screening mammography.- Colonoscopy.- Adaptive Context Selection for Polyp Segmentation.- PraNet: Parallel Reverse Attention Network for Polyp Segmentation.- Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy.- PolypSeg: an Efficient Context-aware Network for Polyp Segmentation from Colonoscopy Videos.- Endoscopic polyp segmentation using a hybrid 2D/3D CNN.- Dermatology.- A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images.- Fairness of Classifiers Across Skin Tones in Dermatology.- Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification.- Clinical-Inspired Network for Skin Lesion Recognition.- Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images.- Fetal Imaging.- Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets.- Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect.- Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency.- Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy.- Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning.- Joint Image Quality Assessment and Brain Extraction of Fetal MRI using Deep Learning.- Heart and Lung Imaging.- Accelerated 4D Respiratory Motion-resolved Cardiac MRI with a Model-based Variational Network.- Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation.- ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation.- A Bottom-up Approach for Real-time Mitral Valve Annulus Modeling on 3D Echo Images.- A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking andS
| Erscheinungsdatum | 04.10.2020 |
|---|---|
| Reihe/Serie | Image Processing, Computer Vision, Pattern Recognition, and Graphics | Lecture Notes in Computer Science |
| Zusatzinfo | XXXVII, 819 p. 33 illus., 1 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1294 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 • Deep learning • Image Analysis • Image Processing • Image Quality • image reconstruction • Image Segmentation • Imaging Systems • Informatics • machine learning • Medical Images • Neural networks • pattern recognition • Research • segmentation methods • Signal Processing |
| ISBN-10 | 3-030-59724-5 / 3030597245 |
| ISBN-13 | 978-3-030-59724-5 / 9783030597245 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
