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|a Information Processing in Medical Imaging
|h [electronic resource] :
|b 27th International Conference, IPMI 2021, Virtual Event, June 28–June 30, 2021, Proceedings /
|c edited by Aasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen.
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|a 1st ed. 2021.
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|b Springer International Publishing :
|b Imprint: Springer,
|c 2021.
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|a XIX, 782 p. 283 illus., 261 illus. in color.
|b online resource.
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|a Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
|v 12729
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|a Registration -- Hypermorph: Amortized Hyperparameter Learning for Image Registration -- Deep learning based geometric registration for medical images: How accurate can we get without visual features -- Diffeomorphic registration with density changes for the analysis of imbalanced shapes -- Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum -- Multiple-shooting adjoint method for whole-brain dynamic causal modeling -- Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models -- Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training -- Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images -- MR Slice Profile Estimation by Learning to Match Internal Patch Distributions -- Partial Matching in the Space of Varifolds -- Nested Grassmanns for Dimensionality Reduction with Applications to Shape Analysis -- Hierarchical Morphology-Guided Tooth Instance Segmentation from CBCT Images -- Cortical Morphometry Analysis based on Worst Transportation Theory -- Geodesic B-Score for Improved Assessment of Knee Osteoarthritis -- Cytoarchitecture Measurements in Brain Gray Matter using Likelihood-Free Inference -- Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping -- Knowledge Transfer for Few-shot Segmentation of Novel White Matter Tracts -- Discovering Spreading Pathways of Neuropathological Events in Alzheimer’s Disease Using Harmonic Wavelets -- A Multi-Scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize The Eloquent Cortex in Brain Tumor Patients -- Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders -- Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data -- Geodesic Tubes for Uncertainty Quantification in Diffusion MRI -- Structural Connectome Atlas Construction in the Space of Riemannian Metrics -- A Higher Order Manifold-valued Convolutional Neural Network with Applications in Diffusion MRI Processing -- Representation Disentanglement for Multi-modal Brain MR Analysis -- Variational Knowledge Distillation for Disease Classification in Chest X-Rays -- Information-based Disentangled Representation Learning for Unsupervised MR Harmonization -- A 3D SegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation -- Unsupervised Learning of Local Discriminative Representation for Medical Images -- TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer -- Segmenting two-dimensional structures with strided tensor networks -- Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation -- Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline -- Feature Library: A Benchmark for Cervical Lesion Segmentation -- Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation.-EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation -- Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography -- A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework -- 3D Nucleus Instance Segmentation for Whole-Brain Microscopy Images -- Teach me to segment with mixed-supervision: confident students become masters -- Sequential modelling -- Future Frame Prediction for Robot-assisted Surgery -- Velocity-To-Pressure (V2P) - Net: Inferring Relative Pressures from Time-Varying 3D Fluid Flow Velocities -- Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces -- Mixture modeling for identifying subtypes in disease course mapping -- Learning transition times in event sequences: the temporal event-based model of disease progression -- Learning with few or low quality labels -- Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays -- Semi-Supervised Screening of COVID-19 from Positive and Unlabeled Data with Constraint Non-Negative Risk Estimator -- Deep MCEM for Weakly-Supervised Learning to Jointly Segment and Recognize Objects using Very Few Expert Segmentations -- Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images -- Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition -- Multimodal Self-Supervised Learning for Medical Image Analysis -- Uncertainty Quantification and Generative Modelling -- Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations -- Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection -- A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations -- Is segmentation uncertainty useful? -- Principled Ultrasound Data Augmentation for Classification of Standard Planes -- Adversarial Regression Learning for Bone Age Estimation -- Learning image quality assessment by reinforcing task amenable data selection -- Collaborative Multi-Agent Reinforcement Learning for Landmark Localization Using Continuous Action Space.
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|a This book constitutes the proceedings of the 27th International Conference on Information Processing in Medical Imaging, IPMI 2021, which was held online during June 28-30, 2021. The conference was originally planned to take place in Bornholm, Denmark, but changed to a virtual format due to the COVID-19 pandemic. The 59 full papers presented in this volume were carefully reviewed and selected from 200 submissions. They were organized in topical sections as follows: registration; causal models and interpretability; generative modelling; shape; brain connectivity; representation learning; segmentation; sequential modelling; learning with few or low quality labels; uncertainty quantification and generative modelling; and deep learning.
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