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Detecting When Pre-trained nnU-Net Models Fail Silently for Covid-19 Lung Lesion Segmentation
24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; 12907 LNCS:304-314, 2021.
Article in English | Scopus | ID: covidwho-1469652
ABSTRACT
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models are not trusted in the clinical routine due to failing silently in out-of-distribution (OOD) data. We propose a lightweight OOD detection method that exploits the Mahalanobis distance in the feature space. The proposed approach can be seamlessly integrated into state-of-the-art segmentation pipelines without requiring changes in model architecture or training procedure, and can therefore be used to assess the suitability of pre-trained models to new data. We validate our method with a patch-based nnU-Net architecture trained with a multi-institutional dataset and find that it effectively detects samples that the model segments incorrectly. © 2021, Springer Nature Switzerland AG.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 Year: 2021 Document Type: Article