Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, D-76344 Eggenstein-Leopoldshafen, Germany.
J Integr Bioinform. 2022 Aug 26;19(4). doi: 10.1515/jib-2022-0018. eCollection 2022 Dec 1.
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.
深度学习模型在图像处理方面能取得高质量的结果。然而，为了稳健地优化深度神经网络的参数，我们需要大型带注释的数据集。图像注释通常由没有全面辅助工具的专家手动完成，这既耗时、繁琐，又不直观。使用这里展示的模块化卡尔斯鲁厄图像处理数据注释 (KaIDA) 工具，现在可以在各种图像处理任务中实现辅助注释，从而在这个过程中为用户提供支持。它旨在简化注释、提高用户效率、增强注释质量，并提供额外的有用的注释相关功能。KaIDA 可在 https://git.scc.kit.edu/sc1357/kaida 获得开源版本。
