1 citations · 1 across the 5 of their papers we have counts for
5 papers
Temporally-Aware Supervised Contrastive Learning for Polyp Counting in Colonoscopy
Luca Parolari, Andrea Cherubini, Lamberto Ballan +1
Automated polyp counting in colonoscopy is a crucial step toward automated procedure reporting and quality control, aiming to enhance the cost-effectiveness of colonoscopy screenin…
Towards Polyp Counting In Full-Procedure Colonoscopy Videos
Luca Parolari, Andrea Cherubini, Lamberto Ballan +1
Automated colonoscopy reporting holds great potential for enhancing quality control and improving cost-effectiveness of colonoscopy procedures. A major challenge lies in the automa…
A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation
Carlo Biffi, Giorgio Roffo, Pietro Salvagnini +1
Following recent advancements in computer-aided detection and diagnosis systems for colonoscopy, the automated reporting of colonoscopy procedures is set to further revolutionize c…
Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing
Giorgio Roffo, Carlo Biffi, Pietro Salvagnini +1
To address overfitting and enhance model generalization in gastroenterological polyp size assessment, our study introduces Feature-Selection Gates (FSG) or Hard-Attention Gates (HA…
REAL-Colon: A dataset for developing real-world AI applications in colonoscopy
Carlo Biffi, Giulio Antonelli, Sebastian Bernhofer +6
Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis…