Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part VI / [electronic resource] :
edited by Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz.
- 1st ed. 2020.
- XXXVII, 819 p. 33 illus., 1 illus. in color. online resource.
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12266 3004-9954 ; .
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12266 .
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 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.
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.
9783030597252
10.1007/978-3-030-59725-2 doi
Computer vision. Artificial intelligence. Social sciences--Data processing. Education--Data processing. Pattern recognition systems. Bioinformatics. Computer Vision. Artificial Intelligence. Computer Application in Social and Behavioral Sciences. Computers and Education. Automated Pattern Recognition. Computational and Systems Biology.