Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures : First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / edited by Hayit Greenspan, Ryutaro Tanno, Marius Erdt, Tal Arbel, Christian Baumgartner, Adrian Dalca, Carole H. Sudre, William M. Wells, Klaus Drechsler, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, Miguel Ángel González Ballester.

Format:

Book

Contributor:

Greenspan, Hayit, editor.

Tanno, Ryutaro, editor.

Erdt, Marius, editor.

Arbel, Tal, editor.

Baumgartner, Christian, editor.

Dalca, Adrian, editor.

Sudre, Carole H., editor.

Wells, William M., editor.

Drechsler, Klaus, editor.

Linguraru, Marius George, editor.

Oyarzun Laura, Cristina, editor.

Shekhar, Raj, editor.

Wesarg, Stefan, editor.

González Ballester, Miguel Ángel, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11840.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11840

Language:

English

Subjects (All):

Artificial intelligence.

Optical data processing.

Medical informatics.

Artificial Intelligence.

Image Processing and Computer Vision.

Health Informatics.

Local Subjects:

Artificial Intelligence.

Image Processing and Computer Vision.

Health Informatics.

Physical Description:

1 online resource (XVII, 192 pages) : 83 illustrations, 76 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 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. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data. .

Contents:

UNSURE 2019: Uncertainty quantification and noise modelling

Probabilistic Surface Reconstruction with Unknown Correspondence

Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty

Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference

Reg R-CNN: Lesion Detection and Grading under Noisy Labels

Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation

Quantifying Uncertainty of deep neural networks in skin lesion classification

UNSURE 2019: Domain shift robustness

A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data

Out of distribution detection for intra-operative functional imaging

CLIP 2019

A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease

Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain

Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis

Recovering physiological changes in nasal anatomy with confidence estimates

Synthesis of Medical Images Using GANs

DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation

Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging

Data Augmentation from Sketch

An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis

A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation

Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes.

Other Format:

Printed edition:

ISBN:

978-3-030-32689-0

9783030326890

Access Restriction:

Restricted for use by site license.