@inproceedings{39e988cf2cc5472d9d4cda199ffecad8,
title = "CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy",
abstract = "Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. The code and pre-trained models for CLTS-GAN are available on Computational Endoscopy Platform GitHub (https://github.com/nadeemlab/CEP ).",
keywords = "Augmentation, Colonoscopy, Polyp detection",
author = "Shawn Mathew and Saad Nadeem and Arie Kaufman",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2022",
doi = "10.1007/978-3-031-16449-1\_49",
language = "English",
isbn = "9783031164484",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "519--529",
editor = "Linwei Wang and Qi Dou and Fletcher, \{P. Thomas\} and Stefanie Speidel and Shuo Li",
booktitle = "Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2022 - 25th International Conference, Proceedings",
}