152 citations · 168 across the 9 of their papers we have counts for
6 papers · 1 filter
Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts
Aakash Tripathi, Ian E. Nielsen, Muhammad Umer +2
Transcription Factor Binding Site (TFBS) prediction is crucial for understanding gene regulation and various biological processes. This study introduces a novel Mixture of Experts…
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran +2
Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improv…
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…
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…