Abstract
The presence of uninformative frames in colonoscopy videos is a major factor that reduces the accuracy and efficiency of various video analysis applications. To address this issue, research on informative frame classification has been conducted, but the lack of a publicly available dataset has made reproducibility difficult. In this study, we propose a novel dataset, InfoColon, which integrates video data collected from multiple medical institutions with major public colonoscopy datasets. All colonoscopy frames were labeled as either an informative frame or one of six types of uninformative frames. We also propose an active learning method to efficiently label large amounts of data with a small initial labeled dataset. Using the constructed InfoColon, we demonstrate the potential for its application in consecutive informative frame classification and 3D reconstruction. We expect that the proposed InfoColon will be valuable for various applications involving colonoscopy video analysis.
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Data availability
The colonoscopy videos, 7-class labels, calibration videos, and parameters for InfoColon have been uploaded and made publicly available on Synapse (https://www.synapse.org/InfoColon). Users must adhere to the data usage terms and conditions of the Synapse platform, and any research utilizing this dataset must cite the present paper.
Code availability
The code required for the data processing, model, and evaluation used in this study has been made publicly available at the following address: https://github.com/Choi-Tae-min/InfoColon.
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Acknowledgements
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A3047535), and was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) [NO.RS-2021-II211343, Artificial Intelligence Graduate School Program (Seoul National University)].
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Taemin Choi was responsible for dataset construction, experiment design, and analysis. Hee Seok Moon and Eun Hyo Jin conducted clinical video acquisition and data labeling validation. Seunghyun Jan and Chang Min Park assisted with data set construction. Dongheon Lee was responsible for research planning, overall project supervision, and guiding the methodology development. All authors were involved in the manuscript preparation and approved the final manuscript.
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Choi, T., Moon, H.S., Jang, S. et al. InfoColon: A dataset for consecutive informative frames in Colonoscopy. Sci Data (2026). https://doi.org/10.1038/s41597-026-07060-2
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DOI: https://doi.org/10.1038/s41597-026-07060-2
