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Wong, C.K.C.* ; Christensen, A.N.* ; Bercea, C.-I. ; Schnabel, J.A. ; Tolsgaard, M.G.* ; Feragen, A.*

Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound.

In: (28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, 23-27 September 2025, Daejeon). Berlin [u.a.]: Springer, 2026. 293-303 (Lect. Notes Comput. Sc. ; 15966 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Reliable out-of-distribution (OOD) detection is important for safe deployment of deep learning models in fetal ultrasound amidst heterogeneous image characteristics and clinical settings. OOD detection relies on estimating a classification model’s uncertainty, which should increase for OOD samples. While existing research has largely focused on uncertainty quantification methods, this work investigates the impact of the classification task itself. Through experiments with eight uncertainty quantification methods across four classification tasks on the same image dataset, we demonstrate that OOD detection performance significantly varies with the task, and that the best task depends on the defined ID-OOD criteria; specifically, whether the OOD sample is due to: i) an image characteristic shift or ii) an anatomical feature shift. Furthermore, we reveal that superior OOD detection does not guarantee optimal abstained prediction, underscoring the necessity to align task selection and uncertainty strategies with the specific downstream application in medical image analysis. Code: https://github.com/wong-ck/ood-fetal-us.
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Publication type Article: Conference contribution
Keywords Fetal Ultrasound ; Ood ; Uncertainty Quantification; Dropout
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Conference Date 23-27 September 2025
Conference Location Daejeon
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 15966 LNCS, Issue: , Pages: 293-303 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of AI for Health (AIH)
Grants Federal Ministry of Research, Technology and Space
DAAD programme Konrad Zuse Schools of Excellence in Artificial Intelligence
Munich Center for Machine Learning
Berdelle-Stiftung
Helmholtz Association under the joint research school 'Munich School for Data Science'
Free State of Bavaria
Danish Regions' AI Signature Project
Danish Pioneer Centre for AI (DNRF)