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Debesh Jha
Researcher at Simula Research Laboratory
Publications - 82
Citations - 2687
Debesh Jha is an academic researcher from Simula Research Laboratory. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 13, co-authored 59 publications receiving 703 citations. Previous affiliations of Debesh Jha include Chosun University.
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Kvasir-SEG: A Segmented Polyp Dataset
Debesh Jha,Pia H. Smedsrud,Michael Riegler,Pål Halvorsen,Thomas de Lange,Dag Johansen,Håvard D. Johansen +6 more
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.
Proceedings ArticleDOI
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
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.
Posted Content
ResUNet++: An Advanced Architecture for Medical Image Segmentation
Debesh Jha,Pia H. Smedsrud,Michael Riegler,Dag Johansen,Thomas de Lange,Pål Halvorsen,Håvard D. Johansen +6 more
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.
Book ChapterDOI
Kvasir-SEG: A Segmented Polyp Dataset
Debesh Jha,Pia H. Smedsrud,Michael Riegler,Pål Halvorsen,Thomas de Lange,Dag Johansen,Håvard D. Johansen +6 more
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.
Proceedings ArticleDOI
ResUNet++: An Advanced Architecture for Medical Image Segmentation
Debesh Jha,Pia H. Smedsrud,Michael Riegler,Dag Johansen,Thomas de Lange,Pål Halvorsen,Håvard D. Johansen +6 more
TL;DR: Wang et al. as mentioned in this paper proposed an improved ResUNet architecture for colonoscopic image segmentation, which achieved a dice coefficient of 81.33% and a mean intersection over union (mIoU) of 79.27% for the Kvasir-SEG dataset.