Thesis Description & Objectives
Deep-learning systems can be accurate on average and still fail on individual medical images. The cause of failure matters: an examination may be degraded by motion or noise, the image may be intrinsically ambiguous even for expert readers, or the case may differ from the training distribution. Current uncertainty-quantification methods usually return a confidence or risk score that is useful for error detection, calibration, selective prediction, or out-of-distribution (OOD) detection. However, the same level of predictive
uncertainty can arise from different failure sources, which may require different responses.
Goal and objectives
The thesis will investigate whether uncertainty can be made failure-source-aware and actionable. The objective is to test whether observable uncertainty signals contain enough information to distinguish clinically meaningful failure factors beyond generic predictive risk.
– Build a benchmark that separates acquisition degradation, reader disagreement / intrinsic ambiguity, and distribution shift
– Compare standard uncertainty signals: softmax confidence/entropy, calibration scores, deep ensembles, Monte-Carlo dropout, test-time augmentation, and representation/OOD scores.
– Evaluate failure-source attribution at matched predictive risk: cases with similar error risk should still be distinguishable by the underlying factor.
– Test whether factor-aware decisions improve simple intervention policies, such as reacquisition/quality control, expert review, or OOD referral, under the same intervention budget.
Project environment, supervision and international mobility
SafeMed is a collaborative research project between GIPSA-lab in Grenoble and the Bio-AI Lab at UiT – The Arctic University of Norway in Tromsø. The activities are based in GIPSA-lab, Grenoble, and are expected to start in February 2027.
