Kvasir-SEG: A Segmented Polyp Dataset
- Debesh Jha, P. Smedsrud, Haavard D. Johansen
- Computer ScienceConference on Multimedia Modeling
- 16 November 2019
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.
ResUNet++: An Advanced Architecture for Medical Image Segmentation
- Debesh Jha, P. Smedsrud, H. Johansen
- Computer ScienceIEEE International Symposium on Multimedia
- 16 November 2019
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.
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
- Debesh Jha, M. Riegler, Dag Johansen, P. Halvorsen, Haavard D. Johansen
- Computer ScienceIEEE 33rd International Symposium on Computer…
- 8 June 2020
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.
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
The HyperKvasir dataset is presented, the largest image and video dataset of the gastrointestinal tract available today and can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.
A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation
- Debesh Jha, P. Smedsrud, M. Riegler
- Computer ScienceIEEE journal of biomedical and health informatics
- 5 January 2021
Improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using Conditional Random Field (CRF) and Test-Time Augmentation (TTA) and it is demonstrated that applying CRF and TTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset.
Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
- Nikhil Kumar Tomar, Debesh Jha, Sharib Ali
- Computer ScienceIEEE Transactions on Neural Networks and Learning…
- 31 March 2021
This work proposes a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch that provides hard attention to the learned feature maps at different convolutional layers.
Kvasir-Capsule, a video capsule endoscopy dataset
The Kvasir-Capsule dataset is presented, a large VCE dataset collected from examinations at a Norwegian Hospital that can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
- Debesh Jha, Nikhil Kumar Tomar, P. Halvorsen
- Computer Science, MedicineIEEE 34th International Symposium on Computer…
- 22 April 2021
This work proposes NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images that allows real-time performance and has higher segmentation accuracy compared to other more complex ones.
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
A comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.