4 papers
CARDINAL Predicts Cardiovascular Risk From Non-contrast Cardiac CT
Roy Gabriel, Nattakorn Kittisut, Jamshid Hassanpour +6
Cardiovascular risk prediction remains limited by incomplete clinical data and imaging biomarkers that reduce computed tomography (CT) to a small number of handcrafted features. We…
SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation
Ali Karkehabadi, Jamshid Hassanpour, Houman Homayoun +1
Gradient-based saliency methods are widely used to interpret deep neural networks, yet they often produce noisy and unstable explanations that poorly align with semantically meanin…
SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G
Hossein Mohammadi, Seyed Bagher Hashemi Natanzi, Ramak Nassiri +3
Dynamic spectrum slicing is a critical enabler for 6G Radio Access Networks (RANs), allowing the coexistence of heterogeneous services. However, optimizing resource allocation in d…
Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation
Jamshid Hassanpour, Vinkle Srivastav, Didier Mutter +1
Self-supervised learning (SSL) approaches have achieved great success when the amount of labeled data is limited. Within SSL, models learn robust feature representations by solving…