Michael Riegler

TL;DR: KVASIR is a dataset containing images from inside the gastrointestinal (GI) tract that contains two categories of images related to endoscopic polyp removal and is important for research on both single and multi-disease computer aided detection.

TL;DR: Kvasir-SEG as mentioned in this paper is an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist.

TL;DR: Encouraging results show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.

TL;DR: ResUNet++ is proposed, which is an improved ResUNet architecture for colonoscopic image segmentation, which significantly outperforms U-Net and Res UNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores.

TL;DR: This paper presents Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist, and demonstrates the use of the dataset with a traditional segmentation approach and a modern deep-learning based Convolutional Neural Network approach.