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KaIDA: a modular tool for assisting image annotation in deep learning

Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehensive tool for assistance which is time- consuming,...

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Detalles Bibliográficos
Autores principales: Schilling, Marcel P., Schmelzer, Svenja, Klinger, Lukas, Reischl, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800041/
https://www.ncbi.nlm.nih.gov/pubmed/36017752
http://dx.doi.org/10.1515/jib-2022-0018
Descripción
Sumario:Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehensive tool for assistance which is time- consuming, burdensome, and not intuitive. Using the here presented modular Karlsruhe Image Data Annotation (KaIDA) tool, for the first time assisted annotation in various image processing tasks is possible to support users during this process. It aims to simplify annotation, increase user efficiency, enhance annotation quality, and provide additional useful annotation-related functionalities. KaIDA is available open-source at https://git.scc.kit.edu/sc1357/kaida.