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2019 | OriginalPaper | Chapter

Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic Lesions

Authors : Akihiro Fukuda, Tadashi Miyamoto, Shunsuke Kamba, Kazuki Sumiyama

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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Abstract

Endoscopy is a standard method for the diagnosis and detection of colorectal lesions. As a method to enhance the detectability of lesions, the effectiveness of pancolonic chromoendoscopy with indigocarmine has been reported. On the other hand, computer-aided diagnosis (CAD) has attracted attention. However, existing CAD systems are mainly for white light imaging (WLI) endoscopy, and the effect of the combination of CAD and indigocarmine dye spraying is not clear. Besides, it is difficult to gather a lot of indigocarmine dye-sprayed (IC) images for training. Here, we propose image-to-image translation from WLI to virtual indigocarmine dye-sprayed (VIC) images based on unpaired cycle-consistent Generative Adversarial Networks. Using this generator as preprocess part, we constructed detection models to evaluate the effectiveness of VIC translation for localization and classification of lesions. We also compared the localization and classification performance with and without image augmentation by using generated VIC images. Our results show that the model trained on IC and VIC images had the highest performance in both localization and classification. Therefore, VIC images are useful for the augmentation of IC images.
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Metadata
Title
Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic Lesions
Authors
Akihiro Fukuda
Tadashi Miyamoto
Shunsuke Kamba
Kazuki Sumiyama
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_12

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