138 citations · 237 across the 4 of their papers we have counts for
4 papers
Deep Learning Generates Synthetic Cancer Histology for Explainability and Education
James M. Dolezal, Rachelle Wolk, Hanna M. Hieromnimon +20
Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or excee…
Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology
James M Dolezal, Andrew Srisuwananukorn, Dmitry Karpeyev +13
A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-wo…
Detecting vortices in superconductors: Extracting one-dimensional topological singularities from a discretized complex scalar field
C. L. Phillips, T. Peterka, D. Karpeyev +1
In type-II superconductors, the dynamics of superconducting vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects…
Stable large-scale solver for Ginzburg-Landau equations for superconductors
I. A. Sadovskyy, A. E. Koshelev, C. L. Phillips +2
Understanding the interaction of vortices with inclusions in type-II superconductors is a major outstanding challenge both for fundamental science and energy applications. At appli…