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Found 10 papers, 1 papers with code
Predicting Generalization of AI Colonoscopy Models to Unseen Data
We test MSN's ability to be trained on data only from Israel and detect unseen techniques, narrow-band imaging (NBI) and chromendoscoy (CE), on colonoscopes from Japan (354 videos, 128 hours).
Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?
Large language models hold significant promise in multilingual applications.
The unreasonable effectiveness of AI CADe polyp detectors to generalize to new countries
$\textbf{Conclusion}$: Differences that prevent CADe detectors from performing well in non-medical settings do not degrade the performance of our AI CADe polyp detector when applied to data from a new country.
Self-Supervised Learning for Endoscopic Video Analysis
To fully exploit the power of SSL, we create sizable unlabeled endoscopic video datasets for training MSNs.
Ranked #2 on
Surgical phase recognition
on Cholec80
Self-Supervised Polyp Re-Identification in Colonoscopy
Computer-aided polyp detection (CADe) is becoming a standard, integral part of any modern colonoscopy system.
Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal Networks
Following the successful debut of polyp detection and characterization, more advanced automation tools are being developed for colonoscopy.
Semi-supervised Quality Evaluation of Colonoscopy Procedures
By integrating the local metric over the withdrawal phase, we build a global, offline quality metric, which is shown to be highly correlated to the standard Polyp Per Colonoscopy (PPC) quality metric.
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings
We demonstrate that this metric more closely aligns with clinician preferences on medical sentences as compared to other metrics (WER, BLUE, METEOR, etc), sometimes by wide margins.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Motion-Based Weak Supervision for Video Parsing with Application to Colonoscopy
We propose a two-stage unsupervised approach for parsing videos into phases.
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles
Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data.
