[CfP] MICCAI 2025 Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE)
Raghav Mehta
Submission deadline 25th June 2025 - https://unsuremiccai.github.io
Overview
With the rise and influence of machine learning (ML) in medical applications and the need to translate newly developed techniques into clinical practice, questions about safety and uncertainty over measurements and reported quantities have gained importance. Obtaining accurate measurements is insufficient, as one needs to establish the circumstances under which these values generalize or give appropriate error bounds for these measures. This is becoming particularly relevant to patient safety as many research groups and companies have deployed or are aiming to deploy ML technology in clinical practice.
The purpose of this workshop is to develop awareness and encourage research on uncertainty modelling to ensure safety for applications spanning both the MIC and CAI fields. In particular, this workshop invites submissions to cover different facets of this topic, including but not limited to: detection and quantification of algorithmic failures; processes of healthcare risk management (e.g. CAD systems); robustness and adaptation to domain shifts; evaluation of uncertainty estimates; defence against noise and mistakes in data (e.g. bias, label mistakes, measurement noise, inter/intra-observer variability). The workshop aims to encourage contributions in a wide range of applications and types of ML algorithms. The use or development of any relevant ML methods is welcomed, including, but not limited to, probabilistic deep learning, Bayesian nonparametric statistics, graphical models, and Gaussian processes. We also aim to ensure broad coverage of applications in the context of both MIC and CAI, which are categorized into reporting problems (descriptions of image contents) such as diagnosis, measurements, segmentation, detection, and enhancement problems (addition of information) such as image synthesis, registration, reconstruction, super-resolution, harmonisation, inpainting and augmented display.
Scope
We accept submissions of original, unpublished work on safety and uncertainty in medical imaging, including (but not limited to) the following areas:
Uncertainty quantification in any MIC or CAI applications
Risk management of ML systems in clinical pipelines
Out-of-distribution and anomaly detection
Defending against hallucinations in enhancement tasks (e.g., super-resolution, reconstruction, modality translation)
Robustness to domain shifts
Measurement errors
Modelling noise in data (e.g., labels, measurements, inter/intra-observer variability)
Validation of uncertainty estimates
Active Learning
Confidence bounds
Conformal prediction
Posterior inference over point estimates
Bayesian deep learning
Graphical models
Gaussian processes
Calibration of uncertainty measures
Bayesian decision theory
Submission Format
Submissions must be at most 8-page papers (excluding references) following the LaTeX or MS Word template. Author names, affiliations, and acknowledgements, as well as any obvious phrasings or clues that can identify authors, must be removed to ensure anonymity. Note that the 8 page limit refers only to the main content. Including references and acknowledgements, the submission may exceed 8 pages. Supplementary material is allowed up to 2 pages. If your application requires the use of video, you can also submit multimedia files. Please note that slide presentations are not allowed as part of the supplementary material.
Please submit papers using the paper submission system
We plan to publish the proceedings as an LNCS volume. All accepted papers will be requested to be presented in person by one of the authors at the workshop and to provide a 66-minute video ahead of the event. Accepted papers will also be invited to submit an extended version to the MELBA journal as part of a special issue.
Best,