2 citations · 2 across the 5 of their papers we have counts for
5 papers
Weakly Supervised Concept Learning with Class-Level Priors for Interpretable Medical Diagnosis
Md Nahiduzzaman, Steven Korevaar, Alireza Bab-Hadiashar +1
Human-interpretable predictions are essential for deploying AI in medical imaging, yet most interpretable-by-design (IBD) frameworks require concept annotations for training data,…
Uncertainty-Aware Information Pursuit for Interpretable and Reliable Medical Image Analysis
Md Nahiduzzaman, Steven Korevaar, Zongyuan Ge +3
To be adopted in safety-critical domains like medical image analysis, AI systems must provide human-interpretable decisions. Variational Information Pursuit (V-IP) offers an interp…
Generalization Capabilities of Neural Cellular Automata for Medical Image Segmentation: A Robust and Lightweight Approach
Steven Korevaar, Ruwan Tennakoon, Alireza Bab-Hadiashar
In the field of medical imaging, the U-Net architecture, along with its variants, has established itself as a cornerstone for image segmentation tasks, particularly due to its stro…
Domain Generalization by Learning from Privileged Medical Imaging Information
Steven Korevaar, Ruwan Tennakoon, Ricky O'Brien +2
Learning the ability to generalize knowledge between similar contexts is particularly important in medical imaging as data distributions can shift substantially from one hospital t…
Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification
Dwarikanath Mahapatra
The success of deep learning has set new benchmarks for many medical image analysis tasks. However, deep models often fail to generalize in the presence of distribution shifts betw…