Abstract
Plenty of computer-aided blind-spot monitoring systems based on the classification model of the upper gastrointestinal sites are developed to enhance the quality of gastroscopy. However, the performance of the white light (WL) based model drops deeply while changing the light source to the special light (SL), a narrowed-spectrum technology. A naive solution is to collect as much data from SL as from WL, but it is hard and time-consuming. In this work, we propose a novel light adaptive module that is only trained by common labeled WL images and unlabeled SL images. Our proposed structure is a plug-in module including a light classification head and a reconstruction decoder. The light classification head is trained in an adversarial manner, which prevents the backbone network to extract light-related features. The reconstruction decoder facilitates the complete preservation of the extracted structural features. The result showed that the original classification model added with our proposed light adaptive module could significantly improve the classification performance under SL and keep the original accuracy under WL, which may help endoscopists achieve better gastroscopy.
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Hao, X., Xu, X., Jiang, D., Zhou, G. (2022). Light Adaptation for Classification of the Upper Gastrointestinal Sites. In: Manfredi, L., et al. Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis. ISGIE GRAIL 2022 2022. Lecture Notes in Computer Science, vol 13754. Springer, Cham. https://doi.org/10.1007/978-3-031-21083-9_1
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DOI: https://doi.org/10.1007/978-3-031-21083-9_1
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