Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 6th International Workshop, UNSURE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

by Carole H. Sudre (Editor), Raghav Mehta (Editor), Cheng Ouyang (Editor), Chen Qin (Editor), Marianne Rakic (Editor)

Overview

This book constitutes the refereed proceedings of the 6th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, on October 10, 2024.

The 20 full papers presented in this book were carefully reviewed and selected from 28 submissions. They are organized in the following topical sections: annotation uncertainty; clinical implementation of uncertainty modelling and risk management in clinical pipelines; out of distribution and domain shift identification and management; uncertainty modelling and estimation.

Product Details

ISBN-13:	9783031731587
Publisher:	Springer-Verlag New York, LLC
Publication date:	10/02/2024
Series:	Lecture Notes in Computer Science , #15167
Sold by:	Barnes & Noble
Format:	eBook
File size:	29 MB
Note:	This product may take a few minutes to download.

Table of Contents

.- Annotation Uncertainty.

.- Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection.

.- Active Learning for Scribble-based Diffusion MRI Segmentation.

.- FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection.

.- Diagnose with Uncertainty Awareness: Diagnostic Uncertainty Encoding Framework for Radiology Report Generation.

.- Clinical implementation of uncertainty modelling and risk management in clinical pipelines.

.- Making Deep Learning Models Clinically Useful - Improving Diagnostic Confidence in Inherited Retinal Disease with Conformal Prediction.

.- GUARDIAN: Guarding Against Uncertainty and Adversarial Risks in Robot-Assisted Surgeries.

.- Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components.

.- Conformal Performance Range Prediction for Segmentation Output Quality Control.

.- Holistic Consistency for Subject-level Segmentation Quality Assessment in Medical Image Segmentation.

.- Out of distribution and domain shift identification and management.

.- CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning.

.- Image-conditioned Diffusion Models for Medical Anomaly Detection.

.- Information Bottleneck-based Feature Weighting for Enhanced Medical Image Out-of-Distribution Detection.

.- Beyond Heatmaps: A Comparative Analysis of Metrics for Anomaly Localization in Medical Images.

.- Typicality excels Likelihood for Unsupervised Out-of-Distribution Detection in Medical Imaging.

.- Evaluating Reliability in Medical DNNs: A Critical Analysis of Feature and Confidence-Based OOD Detection.

.- Uncertainty-Aware Vision Transformers for Medical Image Analysis.

.- Uncertainty modelling and estimation.

.- Efficient Precision control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting.

.- GLANCE: Combating Label Noise using Global and Local Noise Correction for Multi-Label Chest X-ray Classification.

.- Conformal Prediction and Monte Carlo Inference for Addressing Uncertainty in Cervical Cancer Screening.

.- INFORMER- Interpretability Founded Monitoring of Medical Image Deep Learning.