AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation
Meissen F, Kaissis G, Rueckert D (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13166 LNCS
Pages Range: 127-135
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Strasbourg, FRA
ISBN: 9783030972806
DOI: 10.1007/978-3-030-97281-3_19
Abstract
In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for this task. To tackle this problem, we propose AutoSeg, an engine that can generate diverse artificial anomalies that resemble the properties of real-world pathologies. Our method can accurately segment unseen artificial anomalies and outperforms existing methods for pathology detection on a challenging real-world dataset of Chest X-ray images. We experimentally evaluate our method on the Medical Out-of-Distribution Analysis Challenge 2021 (Code available under: https://github.com/FeliMe/autoseg ).
Involved external institutions
Germany (DE)How to cite
APA:
Meissen, F., Kaissis, G., & Rueckert, D. (2022). AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation. In Marc Aubreville, David Zimmerer, Mattias Heinrich (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 127-135). Strasbourg, FRA: Springer Science and Business Media Deutschland GmbH.
MLA:
Meissen, Felix, Georgios Kaissis, and Daniel Rueckert. "AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation." Proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, Strasbourg, FRA Ed. Marc Aubreville, David Zimmerer, Mattias Heinrich, Springer Science and Business Media Deutschland GmbH, 2022. 127-135.
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