11 citations · 26 across the 18 of their papers we have counts for
4 papers · 1 filter
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs
Wei Peng, Tomas Bosschieter, Jiahong Ouyang +4
Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity. However, the mainstay of AI…
Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model
Wei Peng, Ehsan Adeli, Tomas Bosschieter +3
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by M…
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Zixuan Liu, Ehsan Adeli, Kilian M. Pohl +1
Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision pr…
Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs
Jiahong Ouyang, Qingyu Zhao, Edith V Sullivan +4
Many neurological diseases are characterized by gradual deterioration of brain structure and function. Large longitudinal MRI datasets have revealed such deterioration, in part, by…