Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis

This book constitutes the refereed proceedings of the Third International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 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.

PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

Klappentext

This book constitutes the refereed proceedings of the Third Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 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.

PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

Inhalt

UNSURE 2021 - Uncertainty estimation and modelling and annotation uncertainty.- Model uncertainty estimation for medical Imaging based diagnosis.- Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic.- Leveraging uncertainty estimates to improve segmentation performance in cardiac MR.- Improving the reliability of semantic segmentation of medical images by uncertainty modelling with Bayesian deep networks and curriculum learning.- Unpaired MR image homogeneisation by disentangled representations and its uncertainty.- Uncertainty-aware deep learning based deformable registration.- Monte Carlo Concrete DropPath for Epistemic Uncertainty Estimation in Brain Tumour segmentation.- Improving Aleatoric Uncertainty quantification in multi-annotated medical image segmentation with normalizing flows.- UNSURE 2021 - Domain shift robustness and risk management in clinical pipelines.- Task-agnostic out-of-distribution detection using kernel density estimation.- Out of distribution detection for medical images.- Robust selective classification of skin lesions with asymmetric costs.- Confidence-based Out-of-Distribution detection: a comparative study and analysis.- Novel disease detection using ensembles with regularized disagreement.- PIPPI2021.- Automatic Placenta Abnormality Detection using Convolutional Neural Networks on Ultrasound Texture.- Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction.- Spatio-temporal atlas of normal fetal craniofacial feature development and CNN-based ocular biometry for motion-corrected fetal MRI.- Myelination of preterm brain networks at adolescence.- A bootstrap self-training method for sequence transfer: State-of-the-art placenta segmentation in fetal MRI.- Segmentation of the cortical plate in fetal brain MRI with a topological loss.- Fetal brain MRI measurements using a deep learning landmark network with reliability estimation.- CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI.- Detection of Injury and Automated Triage of Preterm Neonatal MRI using Patch-Based Gaussian Processes.- Assessment of Regional Cortical Development through Fissure Based Gestational Age Estimation in 3D Fetal Ultrasound.- Texture-based Analysis of Fetal Organs in Fetal Growth Restriction.- Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI.- Analysis of the Anatomical Variability of Fetal Brains with Corpus Callosum Agenesis.- Predicting preterm birth using multimodal fetal imaging.