152 citations · 168 across the 9 of their papers we have counts for
5 papers · 1 filter
Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond
Jacob R. Epifano, Stephen Glass, Ravi P. Ramachandran +3
This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and bey…
Targeted Background Removal Creates Interpretable Feature Visualizations
Ian E. Nielsen, Erik Grundeland, Joseph Snedeker +2
Feature visualization is used to visualize learned features for black box machine learning models. Our approach explores an altered training process to improve interpretability of…
Revisiting the Fragility of Influence Functions
Jacob R. Epifano, Ravi P. Ramachandran, Aaron J. Masino +1
In the last few years, many works have tried to explain the predictions of deep learning models. Few methods, however, have been proposed to verify the accuracy or faithfulness of…
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
Ian E. Nielsen, Ravi P. Ramachandran, Nidhal Bouaynaya +2
The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning mode…
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review
Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran +2
Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical rec…