Search Results for author:
Found 18 papers, 2 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.
On the Semantic Latent Space of Diffusion-Based Text-to-Speech Models
The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech (TTS) domain is rising, providing great value in synthesizing high quality speech.
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.
Weakly-Supervised Surgical Phase Recognition
A key element of computer-assisted surgery systems is phase recognition of surgical videos.
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.
Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM
Key to our approach is a training objective that jointly supervises speech recognition, text continuation, and speech synthesis using only paired speech-text pairs, enabling a `cross-modal' chain-of-thought within a single decoding pass.
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
Uncertainty quantification for inverse problems in imaging has drawn much attention lately.
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
Weakly-supervised Representation Learning for Video Alignment and Analysis
This paper introduces LRProp -- a novel weakly-supervised representation learning approach, with an emphasis on the application of temporal alignment between pairs of videos of the same action category.
Motion-Based Weak Supervision for Video Parsing with Application to Colonoscopy
We propose a two-stage unsupervised approach for parsing videos into phases.
GoToNet: Fast Monocular Scene Exposure and Exploration
In this work, we present a novel method for real-time environment exploration, whose only requirements are a visually similar dataset for pre-training, enough lighting in the scene, and an on-board forward-looking RGB camera for environmental sensing.
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems
In this work, we introduce image denoisers derived as the gradients of smooth scalar-valued deep neural networks, acting as potentials.
Detecting Deficient Coverage in Colonoscopies
Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.
CFORB: Circular FREAK-ORB Visual Odometry
CFORB has also been run in an indoor environment and achieved an average translational error of $3. 70 \%$.
