A Tutorial on Uncertainty Quantification for Medical Image Analysis
Keywords: Uncertainty Quantification, Medical Image Analysis
TL;DR: We have compiled in an accessible way all the materials from the 2023 and 2024 editions of our beginner-friendly MICCAI tutorial on Uncertainty Quantification for Medical Image Analysis
Abstract: We are hosting this year the second edition of our tutorial on Uncertainty Quantification in Medical Image Analysis, which already took place successfully at MICCAI 2023. In our tutorial we promote discussion within the scientific community in MIC and CAI on the topic of assessing model robustness and on how techniques such as Uncertainty Estimation, Model Calibration, and Conformal Prediction can help both machine learning engineers and clinical practitioners in making reliable decisions.
Beginner-friendly educational materials already available at https://github.com/agaldran/uqinmia-miccai-2023, including slides and hands-on jupyter notebooks on the topics of Domain Shift and Uncertainty Quantification, Uncertainty in Medical Imaging, and Model Calibration.
This year we will be expanding the hands-on sessions, we have added a short introduction on Conformal Prediction, and we will be sharing video recording of all tutorial sessions. The first videos have already been uploaded to youtube*, and we will be completing the upload by the end of the 23rd of August.
* https://www.youtube.com/playlist?list=PLbpn0EkAHGYwEinEq484gYg89st-8Autt
Video: Ongoing upload of tutorial sessions at https://www.youtube.com/playlist?list=PLbpn0EkAHGYwEinEq484gYg89st-8Autt
Website: https://sites.google.com/view/uqinmia-miccai-2024/ --- https://github.com/agaldran/uqinmia-miccai-2023
Submission Number: 18
Loading