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The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12668)

Abstract

The EndoTect challenge at the International Conference on Pattern Recognition 2020 aims to motivate the development of algorithms that aid medical experts in finding anomalies that commonly occur in the gastrointestinal tract. Using HyperKvasir, a large dataset containing images taken from several endoscopies, the participants competed in three tasks. Each task focuses on a specific requirement for making it useful in a real-world medical scenario. The tasks are (i) high classification performance in terms of prediction accuracy, (ii) efficient classification measured by the number of images classified per second, and (iii) pixel-level segmentation of specific anomalies. Hopefully, this can motivate different computer science researchers to help benchmark a crucial component of a future computer-aided diagnosis system, which in turn, could potentially save human lives.

Keywords

  • GI endoscopy
  • Anomaly detection
  • Segmentation
  • Accuracy
  • Efficient processing
  • Challenge

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://github.com/simula/hyper-kvasir.

  2. 2.

    https://github.com/simula/endotect-2020-submission-evaluation.

  3. 3.

    https://pjreddie.com/darknet/tiny-darknet/.

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Hicks, S.A., Jha, D., Thambawita, V., Halvorsen, P., Hammer, H.L., Riegler, M.A. (2021). The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy. In: , et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_18

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  • DOI: https://doi.org/10.1007/978-3-030-68793-9_18

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