Information Processing in Medical Imaging - 
      Aasa Feragen
      Stefan Sommer
      Julia Schnabel
      Mads Nielsen

Information Processing in Medical Imaging

27th International Conference, IPMI 2021, Virtual Event, June 28–June 30, 2021, Proceedings

Aasa Feragen (Redaktør) ; Stefan Sommer (Redaktør) ; Julia Schnabel (Redaktør) ; Mads Nielsen (Redaktør)

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. Les mer
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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.
Forlag: Springer Nature Switzerland AG
Innbinding: Paperback
Språk: Engelsk
Sider: 782
ISBN: 9783030781903
Format: 24 x 16 cm

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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.