1 citations · 1 across the 5 of their papers we have counts for
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Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos
Luca Parolari, Pietro Gori, Lamberto Ballan +2
Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to automated reporting and retrieva…
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…