7 citations · 19 across the 6 of their papers we have counts for
6 papers
Natural Adversarial Objects
Felix Lau, Nishant Subramani, Sasha Harrison +3
Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce…
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models
Zeyad Emam, Andrew Kondrich, Sasha Harrison +4
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). However, since the e…
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
Matthew Groh, Caleb Harris, Luis Soenksen +5
How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision…
ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans
Felix Lau, Tom Hendriks, Jesse Lieman-Sifry +3
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. We cons…
Computationally efficient cardiac views projection using 3D Convolutional Neural Networks
Matthieu Le, Jesse Lieman-Sifry, Felix Lau +3
4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axi…
FastVentricle: Cardiac Segmentation with ENet
Jesse Lieman-Sifry, Matthieu Le, Felix Lau +2
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without aut…