Abstract
Annotating a dataset for training a Supervised Machine Learning algorithm is time and annotator’s attention intensive. Our goal was to create a tool that would enable us to create annotations of the dataset with minimal demands on expert’s time. Inspired by applications such as Tinder, we have created an annotation tool for describing microscopic images. A graphical user interface is used to select from a couple of images the one with the higher value of the examined parameter. Two experiments were performed. The first compares the speed of annotation of our application with the commonly used tool for processing microscopic images. In the second experiment, the texture description was compared with the annotations from MicrAnt application and commonly used application. The results showed that the processing time using our application is 3 times lower and the Spearman coefficient increases by 0.05 than using a commonly used application. In an experiment, we have shown that the annotations processed using our application increase the correlation of the studied parameter and texture descriptors compared with manual annotations .
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Acknowledgments
This work was supported by Charles University Research Centre program UNCE/MED/006 “University Center of Clinical and Experimental Liver Surgery” and Ministry of Education project ITI CZ.02.1.01/0.0/0.0/17_048/0007280: Application of modern technologies in medicine and industry. The research was also supported by the project LO 1506 of the Czech Ministry of Education, Youth and Sports. The authors appreciate the access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the program “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042).
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Jirik, M. et al. (2020). MicrAnt: Towards Regression Task Oriented Annotation Tool for Microscopic Images. In: Lukić, T., Barneva, R., Brimkov, V., Čomić, L., Sladoje, N. (eds) Combinatorial Image Analysis. IWCIA 2020. Lecture Notes in Computer Science(), vol 12148. Springer, Cham. https://doi.org/10.1007/978-3-030-51002-2_15
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DOI: https://doi.org/10.1007/978-3-030-51002-2_15
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