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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022

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Table of Contents

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    Book Overview
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    Chapter 1 Progression Models for Imaging Data with Longitudinal Variational Auto Encoders
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    Chapter 2 Boundary-Enhanced Self-supervised Learning for Brain Structure Segmentation
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    Chapter 3 Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline
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    Chapter 4 3D Global Fourier Network for Alzheimer’s Disease Diagnosis Using Structural MRI
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    Chapter 5 CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis
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    Chapter 6 Interpretable Differential Diagnosis for Alzheimer’s Disease and Frontotemporal Dementia
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    Chapter 7 Is a PET All You Need? A Multi-modal Study for Alzheimer’s Disease Using 3D CNNs
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    Chapter 8 Unsupervised Representation Learning of Cingulate Cortical Folding Patterns
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    Chapter 9 Feature Robustness and Sex Differences in Medical Imaging: A Case Study in MRI-Based Alzheimer’s Disease Detection
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    Chapter 10 Extended Electrophysiological Source Imaging with Spatial Graph Filters
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    Chapter 11 Hybrid Graph Transformer for Tissue Microstructure Estimation with Undersampled Diffusion MRI Data
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    Chapter 12 Atlas-Powered Deep Learning (ADL) - Application to Diffusion Weighted MRI
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    Chapter 13 One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation
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    Chapter 14 Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner
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    Chapter 15 An Adaptive Network with Extragradient for Diffusion MRI-Based Microstructure Estimation
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    Chapter 16 Shape-Based Features of White Matter Fiber-Tracts Associated with Outcome in Major Depression Disorder
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    Chapter 17 White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning
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    Chapter 18 Segmentation of Whole-Brain Tractography: A Deep Learning Algorithm Based on 3D Raw Curve Points
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    Chapter 19 TractoFormer: A Novel Fiber-Level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers
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    Chapter 20 Multi-site Normative Modeling of Diffusion Tensor Imaging Metrics Using Hierarchical Bayesian Regression
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    Chapter 21 Contrastive Functional Connectivity Graph Learning for Population-based fMRI Classification
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    Chapter 22 Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome
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    Chapter 23 Decoding Task Sub-type States with Group Deep Bidirectional Recurrent Neural Network
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    Chapter 24 Hierarchical Brain Networks Decomposition via Prior Knowledge Guided Deep Belief Network
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    Chapter 25 Interpretable Signature of Consciousness in Resting-State Functional Network Brain Activity
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    Chapter 26 Nonlinear Conditional Time-Varying Granger Causality of Task fMRI via Deep Stacking Networks and Adaptive Convolutional Kernels
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    Chapter 27 fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits
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    Chapter 28 Multi-head Attention-Based Masked Sequence Model for Mapping Functional Brain Networks
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    Chapter 29 Dual-HINet: Dual Hierarchical Integration Network of Multigraphs for Connectional Brain Template Learning
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    Chapter 30 RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis
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    Chapter 31 Modelling Cycles in Brain Networks with the Hodge Laplacian
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    Chapter 32 Predicting Spatio-Temporal Human Brain Response Using fMRI
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    Chapter 33 Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer
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    Chapter 34 Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior
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    Chapter 35 Embedding Human Brain Function via Transformer
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    Chapter 36 How Much to Aggregate: Learning Adaptive Node-Wise Scales on Graphs for Brain Networks
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    Chapter 37 Combining Multiple Atlases to Estimate Data-Driven Mappings Between Functional Connectomes Using Optimal Transport
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    Chapter 38 The Semi-constrained Network-Based Statistic (scNBS): Integrating Local and Global Information for Brain Network Inference
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    Chapter 39 Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder
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    Chapter 40 Characterization of Brain Activity Patterns Across States of Consciousness Based on Variational Auto-Encoders
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    Chapter 41 Conditional VAEs for Confound Removal and Normative Modelling of Neurodegenerative Diseases
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    Chapter 42 Semi-supervised Learning with Data Harmonisation for Biomarker Discovery from Resting State fMRI
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    Chapter 43 Cerebral Microbleeds Detection Using a 3D Feature Fused Region Proposal Network with Hard Sample Prototype Learning
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    Chapter 44 Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer’s Disease
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    Chapter 45 AANet: Artery-Aware Network for Pulmonary Embolism Detection in CTPA Images
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    Chapter 46 Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans
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    Chapter 47 What Makes for Automatic Reconstruction of Pulmonary Segments
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    Chapter 48 CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs
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    Chapter 49 Decoupling Predictions in Distributed Learning for Multi-center Left Atrial MRI Segmentation
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    Chapter 50 Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision
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    Chapter 51 Diffusion Deformable Model for 4D Temporal Medical Image Generation
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    Chapter 52 SAPJNet: Sequence-Adaptive Prototype-Joint Network for Small Sample Multi-sequence MRI Diagnosis
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    Chapter 53 Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification
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    Chapter 54 Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View
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    Chapter 55 CheXRelNet : An Anatomy-Aware Model for Tracking Longitudinal Relationships Between Chest X-Rays
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    Chapter 56 Reinforcement Learning for Active Modality Selection During Diagnosis
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    Chapter 57 Ensembled Prediction of Rheumatic Heart Disease from Ungated Doppler Echocardiography Acquired in Low-Resource Settings
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    Chapter 58 Attention Mechanisms for Physiological Signal Deep Learning: Which Attention Should We Take?
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    Chapter 59 Computer-Aided Tuberculosis Diagnosis with Attribute Reasoning Assistance
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    Chapter 60 Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose
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    Chapter 61 RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
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    Chapter 62 A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000
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    Chapter 63 LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection
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    Chapter 64 Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification
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    Chapter 65 Self-Rating Curriculum Learning for Localization and Segmentation of Tuberculosis on Chest Radiograph
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    Chapter 66 Rib Suppression in Digital Chest Tomosynthesis
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    Chapter 67 Multi-task Lung Nodule Detection in Chest Radiographs with a Dual Head Network
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    Chapter 68 Data-Driven Deep Supervision for Skin Lesion Classification
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    Chapter 69 Out-of-Distribution Detection for Long-Tailed and Fine-Grained Skin Lesion Images
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    Chapter 70 FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis
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    Chapter 71 Reliability-Aware Contrastive Self-ensembling for Semi-supervised Medical Image Classification
Attention for Chapter 57: Ensembled Prediction of Rheumatic Heart Disease from Ungated Doppler Echocardiography Acquired in Low-Resource Settings
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Chapter title
Ensembled Prediction of Rheumatic Heart Disease from Ungated Doppler Echocardiography Acquired in Low-Resource Settings
Chapter number 57
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-16431-6_57
Book ISBNs
978-3-03-116430-9, 978-3-03-116431-6
Authors

Roshanitabrizi, Pooneh, Roth, Holger R., Tompsett, Alison, Paulli, Athelia Rosa, Brown, Kelsey, Rwebembera, Joselyn, Okello, Emmy, Beaton, Andrea, Sable, Craig, Linguraru, Marius George

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Researcher 1 33%
Unknown 1 33%
Readers by discipline Count As %
Engineering 2 67%
Unknown 1 33%