Medical Image Computing and Computer Assisted Intervention - Miccai 2020

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 and