Abstract
Large-scale datasets play an important role in the application of deep learning methods to various practical tasks. Many crowdsourcing tools have been proposed for annotation tasks; however, these tasks are relatively easy. Non-obvious annotation tasks require professional knowledge (e.g., medical image annotation) and non-expert annotators need to be trained to perform such tasks. In this paper, we propose Trafne, a framework for the effective training of non-expert annotators by combining feedback from the system (auto validation) and human experts (expert validation). Subsequently, we present a prototype implementation designed for brain tumor image annotation. We perform a user study to evaluate the effectiveness of our framework compared to a traditional training method. The results demonstrate that our proposed approach can help non-expert annotators to complete a non-obvious annotation more accurately than the traditional method. In addition, we discuss the requirements of non-expert training on a non-obvious annotation and potential applications of the framework.
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This work was supported by JST CREST Grant Number JP- MJCR17A1, Japan.
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Miyata, S., Chang, CM., Igarashi, T. (2022). Trafne: A Training Framework for Non-expert Annotators with Auto Validation and Expert Feedback. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2022. Lecture Notes in Computer Science(), vol 13336. Springer, Cham. https://doi.org/10.1007/978-3-031-05643-7_31
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