F. Javier Sánchez

TL;DR: This paper introduces a novel polyp localization method for colonoscopy videos based on a model of appearance for polyps which defines polyp boundaries in terms of valley information and proves that this method outperforms state-of-the-art computational saliency results.

TL;DR: A comparative study is performed to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.

TL;DR: A strategy to adapt real-time polyps detection methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability is proposed.

TL;DR: In this article, the authors introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colorectal cancer image analysis research by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.

TL;DR: GTCreator is introduced, a flexible annotation tool for providing image and text annotations to image-based datasets that is proven to be efficient for large image dataset annotation, as well as showing potential of use in other stages of method evaluation such as experimental setup or results analysis.