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2021 | OriginalPaper | Buchkapitel

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

verfasst von : Steven A. Hicks, Debesh Jha, Vajira Thambawita, Pål Halvorsen, Hugo L. Hammer, Michael A. Riegler

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Nächstes Kapitel A Hierarchical Multi-task Approach to Gastrointestinal Image Analysis

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Titel: The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy
verfasst von: Steven A. Hicks
Debesh Jha
Vajira Thambawita
Pål Halvorsen
Hugo L. Hammer
Michael A. Riegler
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68792-2

Electronic ISBN: 978-3-030-68793-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68793-9_18

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