YOLO networks for polyp detection: A human-in-the-loop training approach

Eixelberger T, Wolkenstein G, Hackner R, Bruns V, Mühldorfer S, Geissler U, Belle S, Wittenberg T (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Current Directions in Biomedical Engineering De Gruyter

Book Volume: 8

Pages Range: 277-280

Journal Issue: 2

DOI: 10.1515/cdbme-2022-1071

Abstract

Introduction: Early detection of adenomas and polyps is one central goal of colonoscopic screening programs. As the adenoma detection rate (ADR) depends on the experience of the endoscopist, AI-based polyp detection systems can be used for real-time assistance. Hence, to support the physicians such AI-based systems using deep-convolutional neural networks (DCNNS) have been introduced in the past years. One disadvantage of these techniques is the need of a huge amount of labeled training data. Method: We investigate a "human-in-the-loop approach" to minimize the required time to generate labeled training data. The approach is evaluated within the training a YOLOv4 neural network to detect polyps in colonoscopic image data. The performance metrics of the neural network are evaluated on three public datasets. Results: The performance of the YOLO network increased from a precision of 0.88, recall of 0.83, F1 score of 0.86, and a F2 score of 0.86 to a precision of 0.91, recall of 0.87, F1 = of 0.89, and F2 = of 0.88. The interactive labelling of 1,000 images only takes one hours. Conclusion: The proposed "human-in-the-loop approach" is capable of generating labelled image data in a minimum of time while increasing the performance metrics as well. For higher performance increase more data can now be labeled within this new approach.

Authors with CRIS profile

Thomas Wittenberg Chair of Visual Computing

Involved external institutions

Klinikum Bayreuth DE Germany (DE) Universitätsklinikum Mannheim DE Germany (DE) Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE) E&L medical systems GmbH DE Germany (DE)

How to cite

APA:

Eixelberger, T., Wolkenstein, G., Hackner, R., Bruns, V., Mühldorfer, S., Geissler, U.,... Wittenberg, T. (2022). YOLO networks for polyp detection: A human-in-the-loop training approach. Current Directions in Biomedical Engineering, 8(2), 277-280. https://dx.doi.org/10.1515/cdbme-2022-1071

MLA:

Eixelberger, Thomas, et al. "YOLO networks for polyp detection: A human-in-the-loop training approach." Current Directions in Biomedical Engineering 8.2 (2022): 277-280.

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