CESP
(Computational Endoscopy, Surgery & Pathology)
Active
Research at CESP focuses on integrating computational techniques into medical practices to enhance the accuracy and effectiveness of endoscopic computer vision, surgical data science, and computational pathology.
Our goal is to improve healthcare outcomes by conducting high throughput imaging and medical image analyses for better diagnostic and surgical interventions.
Focus Area
Impact Area
Research Projects
Publications
2023
An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges
Debesh Jha, ..., Shruti Shrestha, ..., Sharib Ali, Michael A Riegler, Pål Halvorsen, Ulas Bagci, Thomas De Lange
2022
TGANet: Text-guided attention for improved polyp segmentation
Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci, Sharib Ali
2022
Task-Aware Active Learning for Endoscopic Image Analysis
Shrawan Kumar Thapa, Pranav Poudel, Binod Bhattarai, Danail Stoyanov
2022
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Sharib Ali, Noha Ghatwary, Debesh Jha, Ece Isik-Polat, Gorkem Polat, Chen Yang, Wuyang Li, Adrian Galdran, Miguel-Ángel González Ballester, Vajira Thambawita, Steven Hicks, Sahadev Poudel, Sang-Woong Lee, Ziyi Jin, Tianyuan Gan, ChengHui Yu, JiangPeng Yan, Doyeob Yeo, Hyunseok Lee, Nikhil Kumar Tomar, Mahmood Haithmi, Amr Ahmed, Michael A. Riegler, Christian Daul, Pål Halvorsen, Jens Rittscher, Osama E. Salem, Dominique Lamarque, Renato Cannizzaro, Stefano Realdon, Thomas de Lange, James E. East
2021
Iterative deep learning for improved segmentation of endoscopic images
Nikhil Kumar Tomar, Nikhil K Tomar
