9 citations · 9 across the 3 of their papers we have counts for
3 papers
eess.IV2023
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images
Nicholas Konz, Maciej A. Mazurowski
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain p…
stat.CO2023
Robust Chauvenet Rejection: Powerful, but Easy to Use Outlier Detection for Heavily Contaminated Data Sets
Nicholas Konz, Daniel E. Reichart
In Maples et al. (2018) we introduced Robust Chauvenet Outlier Rejection, or RCR, a novel outlier rejection technique that evolves Chauvenet's Criterion by sequentially applying di…
eess.IV2022★ 9 cited
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning
Nicholas Konz, Hanxue Gu, Haoyu Dong +1
The manifold hypothesis is a core mechanism behind the success of deep learning, so understanding the intrinsic manifold structure of image data is central to studying how neural n…