Publikation: Domain Adaptation for Medical Image Seg...
Stammdaten
Titel: | Domain Adaptation for Medical Image Segmentation Using Transformation-Invariant Self-training |
Untertitel: | |
Kurzfassung: | Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have been shown to be highly effective for semi-supervised domain adaptation. However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps. Since the neural network performance should be invariant to image transformations, we look to this fact to identify uncertain pseudo labels. Indeed, we argue that transformation invariant detections can provide more reasonable approximations of ground truth. Accordingly, we propose a semi-supervised learning strategy for domain adaptation termed transformation-invariant self-training (TI-ST). The proposed method assesses pixel-wise pseudo-labels’ reliability and filters out unreliable detections during self-training. We perform comprehensive evaluations for domain adaptation using three different modalities of medical images, two different network architectures, and several alternative state-of-the-art domain adaptation methods. Experimental results confirm the superiority of our proposed method in mitigating the lack of target domain annotation and boosting segmentation performance in the target domain. |
Schlagworte: | Semi-Supervised Learning, Domain Adaptation, Semantic Segmentation, Self Training, Cataract Surgery, MRI, OCT |
Publikationstyp: | Beitrag in Sammelwerk (Autorenschaft) |
Erscheinungsdatum: | 2023 (Print) |
Erschienen in: |
MICCAI '23 Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '23 Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention
(
Springer, Cham;
)
zur Publikation |
Titel der Serie: | Lecture Notes in Computer Science |
Bandnummer: | 14220 |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 331 - 341 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 2023 |
ISBN: |
|
ISSN: | 0302-9743 |
Homepage: | https://link.springer.com/chapter/10.1007/978-3-031-43907-0_32 |
Erscheinungsdatum: | 01.10.2023 |
ISBN (e-book): |
|
eISSN: | 1611-3349 |
DOI: | http://dx.doi.org/10.1007/978-3-031-43907-0_32 |
Homepage: | https://link.springer.com/chapter/10.1007/978-3-031-43907-0_32 |
Open Access |
|
AutorInnen
Negin Ghamsarian (extern) |
Javier Gamazo Tejero (extern) |
Pablo Márquez-Neila (extern) |
Sebastian Wolf (extern) |
Martin Zinkernagel (extern) |
Klaus Schöffmann (intern) |
Raphael Sznitman (extern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
|
AT - 9020 Klagenfurt am Wörthersee |
Kategorisierung
Sachgebiete | |
Forschungscluster | Kein Forschungscluster ausgewählt |
Peer Reviewed |
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Publikationsfokus |
Klassifikationsraster der zugeordneten Organisationseinheiten:
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Arbeitsgruppen |
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Kooperationen
Organisation | Adresse | ||
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Unversity of Bern / Center for AI in Medicine
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CH - 3012 Bern |
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Inselspital Bern / Department of Ophthalmology
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CH - 3010 Bern |
Forschungsaktivitäten
(Achtung: Externe Aktivitäten werden im Suchergebnis nicht mitangezeigt)
Projekte: | Keine verknüpften Projekte vorhanden |
Publikationen: | Keine verknüpften Publikationen vorhanden |
Veranstaltungen: | Keine verknüpften Veranstaltung vorhanden |
Vorträge: | Keine verknüpften Vorträge vorhanden |