Connectionists: [CfP] MICCAI 2024 Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE)

[Raghav Mehta]

*Submission deadline 24th June 2024 - **https://unsuremiccai.github.io*
<https://unsuremiccai.github.io/>




*Overview*With the rise and influence of machine learning (ML) in medical
application 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 are 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We accept submissions of original, unpublished work on *safety and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
   - Posterior inference over point estimates
   - Bayesian deep learning
   - Graphical models
   - Gaussian processes
   - Calibration of uncertainty measures
   - Bayesian decision theory



*Submission Format*

Submissions must be 8-page papers *(excluding references)* following
the Springer
LNCS format
<https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines>.
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.



Please submit papers using the paper submission system
<https://cmt3.research.microsoft.com/UNSURE2024/Submission/>



We plan to publish the proceedings as an LNCS volume. Accepted papers will
also be invited for submission of an extended version to the MELBA journal
as part of a special issue.
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