Chavarrias-Solano, PE, Ali-Teevno, M, Ochoa-Ruiz, G et al. (1 more author) (2022) Improving Artifact Detection in Endoscopic Video Frames Using Deep Learning Techniques. In: Advances in Computational Intelligence 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, Monterrey, Mexico, October 24–29, 2022, Proceedings, Part I. 21st Mexican International Conference on Artificial Intelligence, MICAI 2022, 24-29 Oct 2022, Monterrey, Mexico. Springer , pp. 327-338. ISBN 9783031194924
Abstract
Colorectal cancer cases have been increasing at an alarming rate each year, imposing a healthcare burden worldwide. Multiple efforts have been made to treat this malignancy. However, early screening has been the most promising solution. Optical endoscopy is the primary diagnosis and treatment tool for these malignancies. Even though its success, the endoscopic process represents a challenge due to noisy data, a limited field of view and the presence of multiple artefacts. In this work, we present a comparison between two real-time deep learning frameworks trained to detect artefacts in endoscopic data. Both networks are trained using different data augmentation techniques to analyze their effect when the models are evaluated using data coming from a different distribution. We evaluated these models using the mean average precision (mAP) evaluation metric at a different intersection over union values. Both models outperformed state-of-the-art methods that were evaluated using the same dataset. Also, the use of data augmentation techniques showed an overall improvement in terms of mAP when compared to the case in which no augmentation was applied.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Deep learning ; Object detection; YOLO; YOLACT; Endoscopic artifact detection |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Dec 2022 13:51 |
Last Modified: | 20 Dec 2022 13:51 |
Published Version: | http://dx.doi.org/10.1007/978-3-031-19493-1_26 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-031-19493-1_26 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194316 |