E-grāmata: Medical Image Computing and Computer Assisted Intervention - MICCAI 2022: 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part I

Brain Development and Atlases.- Progression models for imaging data with
Longitudinal Variational Auto Encoders.- Boundary-Enhanced Self-Supervised
Learning for Brain Structure Segmentation.- Domain-Prior-Induced Structural
MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive
Decline.- 3D Global Fourier Network for Alzheimers Disease Diagnosis using
Structural MRI.- CASHformer: Cognition Aware SHape Transformer for
Longitudinal Analysis.- Interpretable differential diagnosis for Alzheimers
disease and Frontotemporal dementia.- Is a PET all you need? A multi-modal
study for Alzheimers disease using 3D CNNs.- Unsupervised Representation
Learning of Cingulate Cortical Folding Patterns.- Feature robustness and sex
differences in medical imaging: a case study in MRI-based Alzheimers disease
detection.- Extended Electrophysiological Source Imaging with Spatial Graph
Filters.- DWI and Tractography.- Hybrid Graph Transformer for Tissue
Microstructure Estimation with Undersampled Diffusion MRI Data.-
Atlas-powered deep learning (ADL) - application to diffusion weighted MRI.-
One-Shot Segmentation of Novel White Matter Tracts via Extensive Data
Augmentation.- Accurate Corresponding Fiber Tract Segmentation via
FiberGeoMap Learner.- An adaptive network with extragradient for diffusion
MRI-based microstructure estimation.- Shape-based features of white matter
fiber-tracts associated with outcome in Major Depression Disorder.- White
Matter Tracts are Point Clouds: Neuropsychological Score Prediction and
Critical Region Localization via Geometric Deep Learning.- Segmentation of
Whole-brain Tractography: A Deep Learning Algorithm Based on 3D Raw Curve
Points.- TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis
Framework Using Spectral Embedding and Vision Transformers.- Multi-site
Normative Modeling of Diffusion Tensor Imaging Metrics Using Hierarchical
Bayesian Regression.- Functional Brain Networks.- Contrastive Functional
Connectivity Graph Learning for Population-based fMRI Classification.- Joint
Graph Convolution for Analyzing Brain Structural and Functional Connectome.-
Decoding Task Sub-type States with Group Deep Bidirectional Recurrent Neural
Network.- Hierarchical Brain Networks Decomposition via Prior Knowledge
Guided Deep Belief Network.- Interpretable signature of consciousness in
resting-state functional network brain activity.- Nonlinear Conditional
Time-varying Granger Causality of Task fMRI via Deep Stacking Networks and
Adaptive Convolutional Kernels.- fMRI Neurofeedback Learning Patterns are
Predictive of Personal and Clinical Traits.- Multi-head Attention-based
Masked Sequence Model for Mapping Functional Brain Networks.- Dual-HINet:
Dual Hierarchical Integration Network of Multigraphs for Connectional Brain
Template Learning.- RefineNet: An Automated Framework to Generate Task and
Subject-Specific Brain Parcellations for Resting-State fMRI Analysis.-
Modelling Cycles in Brain Networks with the Hodge Laplacian.- Predicting
Spatio-Temporal Human Brain Response Using fMRI.- Revealing Continuous Brain
Dynamical Organization with Multimodal Graph Transformer.- Explainable
Contrastive Multiview Graph Representation of Brain, Mind, and Behavior.-
Embedding Human Brain Function via Transformer.- How Much to Aggregate:
Learning Adaptive Node-wise Scales on Graphs for Brain Networks.- Combining
multiple atlases to estimate data-driven mappings between functional
connectomes using optimal transport.- The Semi-constrained Network-Based
Statistic (scNBS): integrating local and global information for brain network
inference.- Unified Embeddings of Structural and Functional Connectome via a
Function-Constrained Structural Graph Variational Auto-Encoder.-
Neuroimaging.- Characterization of brain activity patterns across states of
consciousness based on variational auto-encoders.- Conditional VAEs for
confound removal and normative modelling of neurodegenerative diseases.-
Semi-supervised learning with data harmonisation for biomarker discovery from
resting state fMRI.- Cerebral Microbleeds Detection Using a 3D Feature Fused
Region Proposal Network with Hard Sample Prototype Learning.- Brain-Aware
Replacements for Supervised Contrastive Learning in Detection of Alzheimers
Disease.- Heart and Lung Imaging.- AANet: Artery-Aware Network for Pulmonary
Embolism Detection in CTPA Images.- Siamese Encoder-based Spatial-Temporal
Mixer for Growth Trend Prediction of Lung Nodules on CT Scans.- What Makes
for Automatic Reconstruction of Pulmonary Segments.- CFDA: Collaborative
Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling
of COVID-19 CTs.- Decoupling Predictions in Distributed Learning for
Multi-Center Left Atrial MRI Segmentation.- Scribble-Supervised Medical Image
Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels
Supervision.- Diffusion Deformable Model for 4D Temporal Medical Image
Generation.- SAPJNet: Sequence-Adaptive Prototype-Joint Network for Small
Sample Multi-Sequence MRI Diagnosis.- Evolutionary Multi-objective
Architecture Search Framework: Application to COVID-19 3D CT Classification.-
Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View.-
CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships
between Chest X-Rays.- Reinforcement learning for active modality selection
during diagnosis.- Ensembled Prediction of Rheumatic Heart Disease from
Ungated Doppler Echocardiography Acquired in Low-Resource Settings.-
Attention mechanisms for physiological signal deep learning: which attention
should we take?.- Computer-aided Tuberculosis Diagnosis with Attribute
Reasoning Assistance.- Multimodal Contrastive Learning for Prospective
Personalized Estimation of CT Organ Dose.- RTN: Reinforced Transformer
Network for Coronary CT Angiography Vessel-level Image Quality Assessment.- A
Comprehensive Study of Modern Architectures and Regularization Approaches on
CheXpert5000.-LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule
Detection.- Consistency-based Semi-supervised Evidential Active Learning for
Diagnostic Radiograph Classification.- Self-Rating Curriculum Learning for
Localization and Segmentation of Tuberculosis on Chest Radiograph.- Rib
Suppression in Digital Chest Tomosynthesis.- Multi-Task Lung Nodule Detection
in Chest Radiographs with a Dual Head Network.- Dermatology.- Data-Driven
Deep Supervision for Skin Lesion Classification.- Out-of-Distribution
Detection for Long-tailed and Fine-grained Skin Lesion Images.- FairPrune:
Achieving Fairness Through Pruning for Dermatological Disease Diagnosis.-
Reliability-aware Contrastive Self-ensembling for Semi-supervised Medical
Image Classification.