1k citations · 1k across the 4 of their papers we have counts for
4 papers
Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images
Amir Akbarnejad, Nilanjan Ray, Penny J. Barnes +1
Despite the advances in machine learning and digital pathology, it is not yet clear if machine learning methods can accurately predict molecular information merely from histomorpho…
GPEX, A Framework For Interpreting Artificial Neural Networks
Amir Akbarnejad, Gilbert Bigras, Nilanjan Ray
The analogy between Gaussian processes (GPs) and deep artificial neural networks (ANNs) has received a lot of interest, and has shown promise to unbox the blackbox of deep ANNs. Ex…
Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness t…
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels
Amirhossein Akbarnejad, Mahdieh Soleymani Baghshah
Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with m…