no code implementations • 12 Oct 2023 • Ozdemir Can Kara, Charles Everson, Farshid Alambeigi
Remarkably, the proposed fabrication facilitates the integration and adherence of markers with the gel layer to robustly and reliably obtain a quantitative measure of deformation in real-time regardless of the orientation of ArUco Markers.
no code implementations • 25 Apr 2023 • Siddhartha Kapuria, Tarunraj G. Mohanraj, Nethra Venkatayogi, Ozdemir Can Kara, Yuki Hirata, Patrick Minot, Ariel Kapusta, Naruhiko Ikoma, Farshid Alambeigi
In this study, toward addressing the over-confident outputs of existing artificial intelligence-based colorectal cancer (CRC) polyp classification techniques, we propose a confidence-calibrated residual neural network.
no code implementations • 13 Nov 2022 • Nethra Venkatayogi, Qin Hu, Ozdemir Can Kara, Tarunraj G. Mohanraj, S. Farokh Atashzar, Farshid Alambeigi
In this study, with the goal of reducing the early detection miss rate of colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive vision-based tactile sensor called HySenSe and a complementary and novel machine learning (ML) architecture that explores the potentials of utilizing dilated convolutions, the beneficial features of the ResNet architecture, and the transfer learning concept applied on a small dataset with the scale of hundreds of images.
no code implementations • 8 Nov 2022 • Nethra Venkatayogi, Ozdemir Can Kara, Jeff Bonyun, Naruhiko Ikoma, Farshid Alambeigi
In this study, to address the current high earlydetection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps.
no code implementations • 8 Oct 2019 • Sahba Aghajani Pedram, Peter Walker Ferguson, Changyeob Shin, Ankur Mehta, Erik P. Dutson, Farshid Alambeigi, Jacob Rosen
We propose using a linear approximate Q-learning method in which human knowledge contributes to selecting useful yet simple features of tissue manipulation while the algorithm learns to take optimal actions and accomplish the task.