no code implementations • 17 Jun 2022 • Taylor L. Bobrow, Mayank Golhar, Rohan Vijayan, Venkata S. Akshintala, Juan R. Garcia, Nicholas J. Durr
In this work, we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a high definition clinical colonoscope and high-fidelity colon models for benchmarking computer vision methods in colonoscopy.
no code implementations • 4 May 2022 • Mayank Golhar, Taylor L. Bobrow, Saowanee Ngamruengphong, Nicholas J. Durr
This study demonstrates that synthetic colonoscopy images generated by Generative Adversarial Network (GAN) inversion can be used as training data to improve the lesion classification performance of deep learning models.
no code implementations • 7 Sep 2020 • Mayank Golhar, Taylor L. Bobrow, MirMilad Pourmousavi Khoshknab, Simran Jit, Saowanee Ngamruengphong, Nicholas J. Durr
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.
no code implementations • 29 Jun 2019 • Richard J. Chen, Taylor L. Bobrow, Thomas Athey, Faisal Mahmood, Nicholas J. Durr
Medical endoscopy remains a challenging application for simultaneous localization and mapping (SLAM) due to the sparsity of image features and size constraints that prevent direct depth-sensing.
2 code implementations • 23 Oct 2018 • Taylor L. Bobrow, Faisal Mahmood, Miguel Inserni, Nicholas J. Durr
In images of gastrointestinal tissues, DeepLSR reduces laser speckle noise by 6. 4 dB, compared to a 2. 9 dB reduction from optimized non-local means processing, a 3. 0 dB reduction from BM3D, and a 3. 7 dB reduction from an optical speckle reducer utilizing an oscillating diffuser.