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

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
radiologists 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 andSegmentation in 4D
Echocardiography.- Joint data imputation and mechanistic modelling for
simulating heart-brain interactions in incomplete datasets.- Learning
Geometry-Dependent and Physics-Based Inverse Image Reconstruction.-
Hierarchical Classification of Pulmonary Lesions: A Large-Scale
Radio-Pathomics Study.- Learning Tumor Growth via Follow-Up Volume Prediction
for Lung Nodules.- Multi-stream Progressive Up-sampling Network for Dense CT
Image Reconstruction.- Abnormality Detection in Chest X-ray Images Using
Uncertainty Prediction Autoencoders.- Region Proposals for Saliency Map
Refinement for Weakly-supervised Disease Localisation and Classification.-
CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection
in CT Scans.- Interpretable Identification of Interstitial Lung Diseases
(ILD) Associated Findings from CT.- Learning with Sure Data for Nodule-Level
Lung Cancer Prediction.- Cascaded Robust Learning at Imperfect Labels for
Chest X-ray Segmentation.- Class-Aware Multi-Window Adversarial Lung Nodule
Synthesis Conditioned on Semantic Features.- Nodule2vec: a 3D Deep Learning
System for Pulmonary Nodule Retrieval Using Semantic Representation.- Deep
Active Learning for Effective Pulmonary Nodule Detection.- Musculoskeletal
Imaging.- Towards Robust Bone Age Assessment: Rethinking Label Noise and
Ambiguity.- Improve bone age assessment by learning from anatomical local
regions.- An Analysis by Synthesis Method that Allows Accurate Spatial
Modeling of Thickness of Cortical Bone from Clinical QCT.- Segmentation of
Paraspinal Muscles at Varied Lumbar Spinal Levels by Explicit Saliency-Aware
Learning.- Manifold Ordinal-Mixup for Ordered Classes inTW3-based Bone Age
Assessment.- Contour-based Bone Axis Detection for X-Ray Guided Surgery on
the Knee.- Automatic Segmentation, Localization, and Identification of
Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks.-
Discriminative dictionary-embedded network for comprehensivevertebrae tumor
diagnosis.- Multi-vertebrae segmentation from arbitrary spine MR images under
global view.- A Convolutional Approach to Vertebrae Identification in Whole
Spine MRI.- Keypoints Localization for Joint Vertebra Detection and Fracture
Severity Quantification.- Grading Loss: A Fracture Grade-based Metric Loss
for Vertebral Fracture Detection.- 3D Convolutional Sequence to Sequence
Model for Vertebral Compression Fractures Identification in CT.- SIMBA:
Specific Identity Markers for Bone Age Assessment.- Doctor Imitator: A
Graph-based Bone Age Assessment Framework Using Hand Radiographs.- Inferring
the 3D Standing Spine Posture from 2D Radiographs.- Generative Modelling of
3D in-silico Spongiosa with Controllable Micro-Structural Parameters.-
GAN-based Realistic Bone Ultrasound Image and Label Synthesis for Improved
Segmentation.- Robust Bone Shadow Segmentation from 2D Ultrasound Through
Task Decomposition.