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.
In the last few years, machine learning (ML) techniques have permeated many aspects of the MICCAI community, leading to substantial progress in a wide range of applications ranging from image analysis to surgical assistance. However, in medical applications, algorithms ultimately assist life and death decisions, and translation of such innovations into practice requires a measure of safety. In practice, ML systems often face situations where the correct decision is ambiguous, and therefore principled mechanisms for quantifying uncertainty are required to envision potential practical deployment.
Safety is indeed paramount in medical imaging applications, where images inform scientific conclusions in research, and diagnostic, prognostic or interventional decisions in clinics. However, efforts have mostly focused on improving the accuracy, while systematic approaches ensuring safety of medical imaging-derived automated systems are largely lacking in the existing body of research.
Uncertainty quantification has recently attracted attention in the MICCAI community as a promising approach to provide a reliability metric of the output, and as a mechanism to communicate the knowledge boundary of such ML systems. Spurred on by this emergent interest, the workshop will encourage discussions on the topic of uncertainty modelling and alternative approaches for risk management in a wide range of medical applications. It aims thereby to highlight both fundamental and practical challenges that need to be addressed to achieve safer implementations of ML systems in the clinical world.
We accept submissions of original, unpublished work on safety and uncertainty in medical imaging, including (but not limited to) the following areas:
Our submission guidelines follow the guidelines of the main MICCAI conference. Submissions must be 8-page papers (excluding references) following the Springer LNCS format using 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.
We also allow supplementary materials in the form of a pdf (max 2 pages) for additional proofs or explanatory details. For applications relying on video, multi-media material (e.g. surgery video or cell division) are also allowed. Note: Supplementary material cannot be voiced over slides presenting additional clarifications.
Submission Deadline: July 1, 2026 - 23:59 (PST)
| Name | Affiliation |
|---|---|
| Adrian Galdran | Universitat Pompeu Fabra, Spain |
| Alceu Bissoto | Universität Bern, Switzerland |
| Anna M. Wundram | Universität Luzern, Switzerland |
| Balamurali Murugesan | Amazon, Canada |
| Bernhard Kainz | Imperial College London, UK |
| Bilal Sidiqi | University College London, UK |
| Biraja Ghoshal | University College London, UK |
| Cecilia Diana-Albelda | Universidad Autónoma de Madrid, Spain |
| Chloe He | University College London, UK |
| Damini Rijhwani | Automation Core Inc., USA |
| Disi Lin | Umeå University, Sweden |
| Dwarikanath Mahapatra | Khalifa University of Science, Technology and Research, UAE |
| Ertunc Erdil | ETH Zurich, Switzerland |
| Fons van der Sommen | Eindhoven University of Technology, The Netherlands |
| Francisco Vasconcelos | University College London, UK |
| Gabriel Oliveira-Stahl | University College London, UK |
| Gonzalo Esteban Mosquera Rojas | Erasmus MC, The Netherlands |
| Hongwei Bran Li | National University of Singapore, Singapore |
| Jackie Ma | Fraunhofer HHI, Germany |
| Jacob J. Peoples | Memorial Sloan Kettering Cancer Center, USA |
| James Myles | Imperial College London, UK |
| Jinwei Zhang | Johns Hopkins University, USA |
| Johanna Paula Müller | Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany |
| John McCabe | University College London, UK |
| Krishna Chaitanya | Johnson & Johnson, Switzerland |
| Krishnam Gupta | Audere, USA |
| Leo Joskowicz | Hebrew University of Jerusalem, Israel |
| Liane S. Canas | King's College London, UK |
| Maria Miscouridou | University College London, UK |
| Matt Y. Cheung | Rice University, USA |
| Matthew Baugh | Imperial College London, UK |
| Max-Heinrich Laves | ImFusion, Germany |
| Moritz Fuchs | Bosch Health, Germany |
| Omar Todd | Imperial College London, UK |
| Paul Fischer | Eberhard Karls Universität Tübingen, Germany |
| Peter J.T. Kampen | Technical University of Denmark, Denmark |
| Philip J. Edwards | University College London, UK |
| Rahul Venkataramani | GE Healthcare, India |
| Robbert Roel Struyven | Harvard University, USA |
| Rui W. Yeow | University College London, UK |
| Shahab Aslani | University College London, UK |
| Shelley Zixin Shu | Universität Bern, Switzerland |
| Shishuai Wang | Erasmus MC, The Netherlands |
| Simon Baur | Fraunhofer, Germany |
| Thomas Schultz | University of Bonn, Germany |
| Tim Flühmann | Universität Bern, Switzerland |
| Tristan Glatard | Concordia University, Canada |
| Ufaq Khan | Mohamed Bin Zayed University of Artificial Intelligence, UAE |
| William Consagra | University of South Carolina, USA |
| Xinzhe Luo | Imperial College London, UK |
| Yasin Ibrahim | University of Oxford, UK |
| Yipeng Hu | University College London, UK |
| Yong Xia | Northwestern Polytechnical University, China |
| Zeinab Abboud | École Polytechnique de Montréal, Canada |