A Lightweight Framework for Semantic Segmentation of Biomedical Images
Abstract: We introduce a lightweight framework for semantic segmentation that utilizes structured classifiers as an alternative to deep learning methods. Biomedical data is known for being scarce and difficult to label. However, this framework provides a lightweight, easy-to-apply, and fast-to-train approach that can be adapted to changes in image material though efficient retraining. Moreover, the framework is able to adapt to various input sizes making it robust against changes in resolution and is not tied to specialized hardware, which allows efficient application on standard laptops or desktops without GPUs. We benchmark two distinct models, a single structured classifier and an ensemble of structured classifiers, against a U-Net, evaluating overall performance and training speed. The framework is versatile and can be applied to multi-class semantic segmentation. Our study shows that the proposed framework can effectively compete with established deep learning methods on diverse datasets in terms of performance while reducing training time immensely.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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A Lightweight Framework for Semantic Segmentation of Biomedical Images ; volume:9 ; number:1 ; year:2023 ; pages:190-193 ; extent:4
Current directions in biomedical engineering ; 9, Heft 1 (2023), 190-193 (gesamt 4)
- Urheber
- DOI
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10.1515/cdbme-2023-1048
- URN
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urn:nbn:de:101:1-2023092214233907266580
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 14.08.2025, 10:56 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Rieken Münke, Friedrich
- Rettenberger, Luca
- Reischl, Markus
- Popova, Anna
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