Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis : Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings / edited by Carole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, Sarah Parisot.

Format:

Book

Contributor:

Sudre, Carole H., Editor.

Fehri, Hamid., Editor.

Arbel, Tal, Editor.

Baumgartner, Christian F., Editor.

Dalca, Adrian., Editor.

Tanno, Ryutaro, Editor.

Van Leemput, Koen., Editor.

Wells, William M., Editor.

Sotiras, Aristeidis., Editor.

Papiez, Bartlomiej., Editor.

Ferrante, Enzo, Editor.

Parisot, Sarah., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12443

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12443

Language:

English

Subjects (All):

Artificial intelligence.

Pattern recognition systems.

Computer vision.

Social sciences-Data processing.

Artificial Intelligence.

Automated Pattern Recognition.

Computer Vision.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Artificial Intelligence.

Automated Pattern Recognition.

Computer Vision.

Computer Application in Social and Behavioral Sciences.

Physical Description:

1 online resource (XVII, 222 pages) : 85 illustrations, 76 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

Contents:

UNSURE 2020

Image registration via stochastic gradient Markov chain Monte Carlo

RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification

Hierarchical brain parcellation with uncertainty

Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-Class Segmentation

Uncertainty Estimation in Landmark Localization based on Gaussian Heatmaps

Weight averaging impact on the uncertainty of retinal artery-venous segmentation

Improving Pathological Distribution Measurements with Bayesian Uncertainty

Improving Reliability of Clinical Models using Prediction Calibration

Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability

GRAIL 2020

Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates

Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences

Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders

Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Proling

Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation

Min-cut Max-flow for Network Abnormality Detection: Application to Preterm Birth

Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface

The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification

Intraoperative Liver Surface Completion with Graph Convolutional VAE

HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification.

Other Format:

Printed edition:

ISBN:

978-3-030-60365-6

9783030603656

Access Restriction:

Restricted for use by site license.