Information Processing in Medical Imaging : 27th International Conference, IPMI 2021, Virtual Event, June 28-June 30, 2021, Proceedings / edited by Aasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen.

Format:

Book

Contributor:

Feragen, Aasa, Editor.

Sommer, Stefan, Editor.

Schnabel, Julia., Editor.

Nielsen, Mads, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12729

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12729

Language:

English

Subjects (All):

Computer vision.

Computer Vision.

Local Subjects:

Computer Vision.

Physical Description:

1 online resource (XIX, 782 pages) : 283 illustrations, 261 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-78191-0

9783030781910

Access Restriction:

Restricted for use by site license.