<div dir="ltr"><div><font face="verdana, sans-serif"><span id="gmail-docs-internal-guid-d8c1d1d6-7fff-d37c-7df7-f40b9bcaa12d"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Submission deadline 25</span><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"><span style="font-size:0.6em;vertical-align:super">th</span></span><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> June 2025 - </span><a href="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" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;text-decoration-line:underline;vertical-align:baseline">https://unsuremiccai.github.io</span></a></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;color:rgb(17,85,204);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;text-decoration-line:underline;vertical-align:baseline"><br></span><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Overview</span><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"><br></span><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">With the rise and influence of machine learning (ML) in medical application<span class="gmail_default" style="font-family:verdana,sans-serif">s</span> 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.</span><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"><br></span><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"><br></span><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">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 <span class="gmail_default" style="font-family:verdana,sans-serif"></span>i<span class="gmail_default" style="font-family:verdana,sans-serif">s</span> welcomed, including, but not limited to, probabilistic deep learning, Bayesian nonparametric statistics, graphical models<span class="gmail_default" style="font-family:verdana,sans-serif">,</span> 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.</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:15pt"><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Scope</span></p><p dir="ltr" style="line-height:1.65;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">We accept submissions of original, unpublished work on </span><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">safety and uncertainty in medical imaging</span><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">, including (but not limited to) the following areas:</span></p><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Uncertainty quantification in any MIC or CAI applications </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Risk management of ML systems in clinical pipelines </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Out-of-distribution and anomaly detection </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Defending against hallucinations in enhancement tasks (e.g.<span class="gmail_default" style="font-family:verdana,sans-serif">,</span> super-resolution, reconstruction, modality translation) </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Robustness to domain shifts </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Measurement errors </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Modelling noise in data (e.g.<span class="gmail_default" style="font-family:verdana,sans-serif">,</span> labels, measurements, inter/intra-observer variability) </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Validation of uncertainty estimates </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Active Learning </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Confidence bounds </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Conformal prediction</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Posterior inference over point estimates </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Bayesian deep learning</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Graphical models </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Gaussian processes </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Calibration of uncertainty measures </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" role="presentation"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Bayesian decision theory </span></p></li></ul><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:15pt"><span style="font-size:11pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Submission Format</span></p><p dir="ltr" style="line-height:1.65;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Submissions must be at most 8-page papers </span><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">(excluding references)</span><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> following the </span><a href="https://conferences.miccai.org/2025/files/downloads/MICCAI2025-LaTeX-Template.zip" style="text-decoration-line:none"><span style="font-size:11.5pt;color:rgb(0,0,255);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;text-decoration-line:underline;vertical-align:baseline">LaTeX</span></a><span style="font-size:11.5pt;color:rgb(18,18,18);font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> or </span><a href="https://conferences.miccai.org/2025/files/downloads/MICCAI2025-Word-Template.zip" style="text-decoration-line:none"><span style="font-size:11.5pt;color:rgb(0,0,255);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;text-decoration-line:underline;vertical-align:baseline">MS Word</span></a><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> template. Author names, affiliations<span class="gmail_default" style="font-family:verdana,sans-serif">,</span> and acknowledgements, as well as any obvious phrasings or clues that can identify authors<span class="gmail_default" style="font-family:verdana,sans-serif">,</span> must be removed to ensure anonymity. Note that the 8 page limit refers only to the main content. Including references and acknowledgements<span class="gmail_default" style="font-family:verdana,sans-serif">,</span> the submission may exceed 8 pages. Supplementary material is allowed <span class="gmail_default" style="font-family:verdana,sans-serif"></span>u<span class="gmail_default" style="font-family:verdana,sans-serif">p</span> <span class="gmail_default" style="font-family:verdana,sans-serif"></span>t<span class="gmail_default" style="font-family:verdana,sans-serif">o</span> 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.</span></p><p dir="ltr" style="line-height:1.65;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> </span></p><p dir="ltr" style="line-height:1.65;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Please submit papers using the </span><a href="https://cmt3.research.microsoft.com/UNSURE2025/Submission/" style="text-decoration-line:none"><span style="font-size:12pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;text-decoration-line:underline;vertical-align:baseline">paper submission system</span></a></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:12pt;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">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 6<span class="gmail_default" style="font-family:verdana,sans-serif"></span><span class="gmail_default" style="font-family:verdana,sans-serif">6-minute</span> video ahead of the event. Accepted papers will also be invited <span class="gmail_default" style="font-family:verdana,sans-serif"></span>t<span class="gmail_default" style="font-family:verdana,sans-serif">o</span> submi<span class="gmail_default" style="font-family:verdana,sans-serif"></span>t an extended version to the MELBA journal as part of a special issue. </span></p></span><br><span class="gmail_default" style="font-family:verdana,sans-serif">Best,</span><br class="gmail-Apple-interchange-newline"></font></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><font face="verdana, sans-serif">Raghav Mehta<span class="gmail_default" style="font-family:verdana,sans-serif"></span>,<span class="gmail_default" style="font-family:verdana,sans-serif"> on behalf of the UNSURE 2025 organizing committee </span><br>Webpage: <a href="http://ragmeh11.github.io" target="_blank" style="">ragmeh11.github.io</a></font></div></div></div></div>