4 papers · 1 filter
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…