70 citations · 163 across the 18 of their papers we have counts for
7 papers · 1 filter
Predicting Generalization in Deep Learning via Local Measures of Distortion
Abhejit Rajagopal, Vamshi C. Madala, Shivkumar Chandrasekaran +1
We study generalization in deep learning by appealing to complexity measures originally developed in approximation and information theory. While these concepts are challenged by th…
multiMap: A Gradient Spoiled Sequence for Simultaneously Measuring B1+, B0, T1/M0, T2, T2*, and Fat Fraction of a Slice
Nicholas Dwork, Adam B. Kerr, Ethan M. I. Johnson +5
We propose multiMap, a single scan that can generate several quantitative maps simultaneously. The sequence acquires multiple images in a time-efficient manner, which can be modele…
Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization
Nicholas Dwork, Ethan M. I. Johnson, Daniel O'Connor +5
With the advent of multi-coil imaging and compressed sensing, a number of model based reconstruction algorithms have been created. They incorporate a multitude of different regular…
Fast Variable Density Poisson-Disc Sample Generation with Directional Variation
Nicholas Dwork, Corey A. Baron, Ethan M. I. Johnson +3
We present a fast method for generating random samples according to a variable density Poisson-disc distribution. A minimum threshold distance is used to create a background grid a…
Development of Conditional Random Field Insert for UNet-based Zonal Prostate Segmentation on T2-Weighted MRI
Peng Cao, Susan M. Noworolski, Olga Starobinets +6
Purpose: A conventional 2D UNet convolutional neural network (CNN) architecture may result in ill-defined boundaries in segmentation output. Several studies imposed stronger constr…
Utilizing the Wavelet Transform's Structure in Compressed Sensing
Nicholas Dwork, Daniel O'Connor, Corey A. Baron +4
Compressed sensing has empowered quality image reconstruction with fewer data samples than previously though possible. These techniques rely on a sparsifying linear transformation.…