110 citations · 150 across the 5 of their papers we have counts for
6 papers
C3VD-DEFCOL: A Deformable Colonoscopy Dataset with Time-Resolved 3D Ground Truth and Realistic Appearance
Ethan Luk, Mayank V. Golhar, Anthony Song +5
3D reconstruction could improve colonoscopy by estimating mucosal coverage and alerting clinicians to missed regions during screening. However, algorithm development is limited as…
C3VDv2 -- Colonoscopy 3D video dataset with enhanced realism
Mayank V. Golhar, Lucas Sebastian Galeano Fretes, Loren Ayers +3
Spatial computer vision techniques have the potential to improve the diagnostic performance of colonoscopy. However, the lack of 3D colonoscopy datasets for training and validation…
Multi-contrast laser endoscopy for in vivo gastrointestinal imaging
Taylor L. Bobrow, Mayank Golhar, Suchapa Arayakarnkul +3
White light endoscopy is the clinical gold standard for detecting diseases in the gastrointestinal tract. Most applications involve identifying visual abnormalities in tissue color…
Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration
Taylor L. Bobrow, Mayank Golhar, Rohan Vijayan +3
Screening colonoscopy is an important clinical application for several 3D computer vision techniques, including depth estimation, surface reconstruction, and missing region detecti…
GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification
Mayank Golhar, Taylor L. Bobrow, Saowanee Ngamruengphong +1
A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study demonstrates that synthetic colonoscopy images generated by Generative A…
Improving colonoscopy lesion classification using semi-supervised deep learning
Mayank Golhar, Taylor L. Bobrow, MirMilad Pourmousavi Khoshknab +3
While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent…