Computer implemented method for detecting an out-of-distribution case, a computer implemented method for training an epistemic bayesian uncertainty model, a data processing device, an imaging system, a computer program product and a computer readable medium

A computer-implemented method for detecting an out-of-distribution case, the computer-implemented method comprising:

• receiving medical imaging data by an input interface of a data processing device, the medical imaging data including voxels and representing an anatomical region including an object set, the object set including at least one anatomical object;
• applying, by a computation unit, an epistemic Bayesian uncertainty model to the medical imaging data to determine epistemic uncertainty information describing an epistemic uncertainty of assignment information describing an assignment of a respective voxel to a respective anatomical object;
• applying a scoring procedure to the epistemic uncertainty information to determine scoring information;
• providing, by an output interface, a warning signal in response to the scoring information satisfying an out-of-distribution condition;
• wherein
• the epistemic Bayesian uncertainty model includes a deep ensemble model including base learners,
• each of the base learners is configured to determine weak assignment information describing an assignment of the respective voxel to the respective anatomical object in a respective forward pass,
• each of the base learners is trained on a respective subset of training data,
• each of the base learners includes Monte Carlo dropout layers,
• the epistemic Bayesian uncertainty model is configured to perform a respective Monte Carlo dropout of the Monte Carlo dropout layers in the respective forward pass, and
• the epistemic Bayesian uncertainty model is configured to determine the epistemic uncertainty information for the respective voxel based on a variance of the weak assignment information provided for the respective voxel in respective forward passes by the base learners.