no code implementations • 7 Nov 2021 • Felix Lau, Nishant Subramani, Sasha Harrison, Aerin Kim, Elliot Branson, Rosanne Liu
Moreover, by comparing a variety of object detection architectures, we find that better performance on MSCOCO validation set does not necessarily translate to better performance on NAO, suggesting that robustness cannot be simply achieved by training a more accurate model.
Ranked #1 on Object Detection on NAO
no code implementations • 31 Jul 2021 • Zeyad Emam, Andrew Kondrich, Sasha Harrison, Felix Lau, Yushi Wang, Aerin Kim, Elliot Branson
High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL).
2 code implementations • 20 Apr 2021 • Matthew Groh, Caleb Harris, Luis Soenksen, Felix Lau, Rachel Han, Aerin Kim, Arash Koochek, Omar Badri
We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on.
no code implementations • 14 Aug 2018 • Felix Lau, Tom Hendriks, Jesse Lieman-Sifry, Berk Norman, Sean Sall, Daniel Golden
Medical images with specific pathologies are scarce, but a large amount of data is usually required for a deep convolutional neural network (DCNN) to achieve good accuracy.
no code implementations • 3 Nov 2017 • Matthieu Le, Jesse Lieman-Sifry, Felix Lau, Sean Sall, Albert Hsiao, Daniel Golden
We demonstrate that the long and short axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist.
no code implementations • 13 Apr 2017 • Jesse Lieman-Sifry, Matthieu Le, Felix Lau, Sean Sall, Daniel Golden
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function.