UNSURE2022 special issue

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 the workshop was to develop awareness and encourage research on uncertainty modelling to ensure safety for applications spanning both the Medical Image Computing and and Computer Aided Intervention fields. The workshop invited 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 aimed to encourage contributions in a wide range of applications and types of ML algorithms. The use or development of any relevant ML methods were welcomed, including, but not limited to, probabilistic deep learning, Bayesian nonparametric statistics, graphical models and Gaussian processes. We also aimed 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.

This special issue represents the best of the contributions from the workshop – the articles are substantially expanded over the workshop versions, and they have been re-reviewed as journal articles. We think the the topic is important and we expect readers will find the contributions to be interesting.

Workshop page

1 paper