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ORCIDUNSURE@MICCAI 2022: Singapore
- Carole H. Sudre
, Christian F. Baumgartner, Adrian V. Dalca, Chen Qin, Ryutaro Tanno, Koen Van Leemput, William M. Wells III:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging - 4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. Lecture Notes in Computer Science 13563, Springer 2022, ISBN 978-3-031-16748-5
Uncertainty Modelling
- Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna, Nicholas Ayache, Hervé Delingette:
MOrphologically-Aware Jaccard-Based ITerative Optimization (MOJITO) for Consensus Segmentation. 3-13 - Katarína Tóthová, Lubor Ladicky, Daniel Thul, Marc Pollefeys, Ender Konukoglu:
Quantification of Predictive Uncertainty via Inference-Time Sampling. 14-25 - Luke Whitbread, Mark Jenkinson:
Uncertainty Categories in Medical Image Segmentation: A Study of Source-Related Diversity. 26-35 - Martin Van Waerebeke, Gregory A. Lodygensky, Jose Dolz:
On the Pitfalls of Entropy-Based Uncertainty for Multi-class Semi-supervised Segmentation. 36-46 - Gangin Park, Chunsan Hong, Bohyung Kim, Won Hwa Kim:
What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation? 47-56
Uncertainty Calibration
- Cheng Ouyang, Shuo Wang, Chen Chen, Zeju Li, Wenjia Bai, Bernhard Kainz, Daniel Rueckert:
Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation. 59-69 - Prerak Mody, Nicolas F. Chaves-de-Plaza, Klaus Hildebrandt, Marius Staring:
Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty. 70-79 - Hariharan Ravishankar, Rohan Patil, Deepa Anand, Vanika Singhal, Utkarsh Agrawal, Rahul Venkataramani, Prasad Sudhakar:
Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty. 80-88 - Jacob Carse, Andres Alvarez Olmo, Stephen J. McKenna:
Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopathology Datasets. 89-99
Annotation Uncertainty and Out of Distribution Management
- Matthew Baugh, Jeremy Tan, Athanasios Vlontzos, Johanna P. Müller, Bernhard Kainz:
nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods. 103-112 - Ishaan Bhat, Josien P. W. Pluim, Hugo J. Kuijf:
Generalized Probabilistic U-Net for Medical Image Segementation. 113-124 - Parinaz Roshanzamir, Hassan Rivaz, Joshua Ahn, Hamza Mirza, Neda Naghdi, Meagan Anstruther, Michele C. Battié, Maryse Fortin, Yiming Xiao:
Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-task TransUNet. 125-134 - Raghav Mehta, Changjian Shui, Brennan Nichyporuk, Tal Arbel:
Information Gain Sampling for Active Learning in Medical Image Classification. 135-145
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