Connectionists: [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
<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Funsuremiccai.github.io%2F&data=05%7C01%7Ccarole.sudre%40KCL.AC.UK%7C5bdfecf528774e6bedcc08da2f2884ac%7C8370cf1416f34c16b83c724071654356%7C0%7C0%7C637874148925669097%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=q4nOzApOhtaMmNe1ZEOkMNwb%2B2lHevK6z%2BGsDwh4Vc4%3D&reserved=0>


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
<https://conferences.miccai.org/2025/files/downloads/MICCAI2025-LaTeX-Template.zip>
or MS Word
<https://conferences.miccai.org/2025/files/downloads/MICCAI2025-Word-Template.zip>
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
<https://cmt3.research.microsoft.com/UNSURE2025/Submission/>



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,
Raghav Mehta, on behalf of the UNSURE 2025 organizing committee
Webpage: ragmeh11.github.io
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