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Creators/Authors contains: "Saad Nadeem"

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  1. ; ; (, Springer)
    Automatedanalysisofopticalcolonoscopy(OC)videoframes (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods ei- ther remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expen- sive 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). 
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    Accepted Manuscript Full Text Available