40 citations · 40 across the 8 of their papers we have counts for
12 papers
Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance
Mohamed Mohamed, Brennan Nichyporuk, Douglas L. Arnold +1
Vision-language models have demonstrated impressive capabilities in generating 2D images under various conditions; however, the success of these models is largely enabled by extens…
Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses
Emily Kaczmarek, Justin Szeto, Brennan Nichyporuk +1
3D structural Magnetic Resonance Imaging (MRI) brain scans are commonly acquired in clinical settings to monitor a wide range of neurological conditions, including neurodegenerativ…
SSL-AD: Spatiotemporal Self-Supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets
Emily Kaczmarek, Justin Szeto, Brennan Nichyporuk +1
Alzheimer's disease is a progressive, neurodegenerative disorder that causes memory loss and cognitive decline. While there has been extensive research in applying deep learning mo…
Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
Gian Mario Favero, Ge Ya Luo, Nima Fathi +5
Image-based personalized medicine has the potential to transform healthcare, particularly for diseases that exhibit heterogeneous progression such as Multiple Sclerosis (MS). In th…
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Gian Mario Favero, Parham Saremi, Emily Kaczmarek +2
Discriminative classifiers have become a foundational tool in deep learning for medical imaging, excelling at learning separable features of complex data distributions. However, th…
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Nima Fathi, Amar Kumar, Brennan Nichyporuk +2
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor g…