Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification

High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-superv...

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Detalles Bibliográficos
Autores principales: Taleb, Aiham, Rohrer, Csaba, Bergner, Benjamin, De Leon, Guilherme, Rodrigues, Jonas Almeida, Schwendicke, Falk, Lippert, Christoph, Krois, Joachim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140204/
https://www.ncbi.nlm.nih.gov/pubmed/35626392
http://dx.doi.org/10.3390/diagnostics12051237

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140204/
https://www.ncbi.nlm.nih.gov/pubmed/35626392
http://dx.doi.org/10.3390/diagnostics12051237

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