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Titel:

Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation.

Dokumenttyp:
Journal Article
Autor(en):
Ouyang, Cheng; Chen, Chen; Li, Surui; Li, Zeju; Qin, Chen; Bai, Wenjia; Rueckert, Daniel
Abstract:
Deep learning models usually suffer from the domain shift issue, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data are only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentati...     »
Zeitschriftentitel:
IEEE Trans Med Imaging
Jahr:
2023
Band / Volume:
42
Heft / Issue:
4
Seitenangaben Beitrag:
1095-1106
Volltext / DOI:
doi:10.1109/TMI.2022.3224067
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/36417741
Print-ISSN:
0278-0062
TUM Einrichtung:
Institut für KI und Informatik in der Medizin
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