26 citations · 55 across the 8 of their papers we have counts for
8 papers
Self-Supervised Learning for Endoscopic Video Analysis
Roy Hirsch, Mathilde Caron, Regev Cohen +6
Self-supervised learning (SSL) has led to important breakthroughs in computer vision by allowing learning from large amounts of unlabeled data. As such, it might have a pivotal rol…
Self-Supervised Polyp Re-Identification in Colonoscopy
Yotam Intrator, Natalie Aizenberg, Amir Livne +2
Computer-aided polyp detection (CADe) is becoming a standard, integral part of any modern colonoscopy system. A typical colonoscopy CADe detects a polyp in a single frame and does…
Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal Networks
Ori Kelner, Or Weinstein, Ehud Rivlin +1
Following the successful debut of polyp detection and characterization, more advanced automation tools are being developed for colonoscopy. The new automation tasks, such as qualit…
Semi-supervised Quality Evaluation of Colonoscopy Procedures
Idan Kligvasser, George Leifman, Roman Goldenberg +2
Colonoscopy is the standard of care technique for detecting and removing polyps for the prevention of colorectal cancer. Nevertheless, gastroenterologists (GI) routinely miss appro…
Colonoscopy Coverage Revisited: Identifying Scanning Gaps in Real-Time
G. Leifman, I. Kligvasser, R. Goldenberg +2
Colonoscopy is the most widely used medical technique for preventing Colorectal Cancer, by detecting and removing polyps before they become malignant. Recent studies show that arou…
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings
Joel Shor, Ruyue Agnes Bi, Subhashini Venugopalan +3
Automatic Speech Recognition (ASR) in medical contexts has the potential to save time, cut costs, increase report accuracy, and reduce physician burnout. However, the healthcare in…
Motion-Based Weak Supervision for Video Parsing with Application to Colonoscopy
Ori Kelner, Or Weinstein, Ehud Rivlin +1
We propose a two-stage unsupervised approach for parsing videos into phases. We use motion cues to divide the video into coarse segments. Noisy segment labels are then used to weak…
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles
Dror Simon, Miriam Farber, Roman Goldenberg
Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data. Wrong or inaccurate annotations generated by the ensemble may lead to performance degrad…