Research
- Develop AI-based longitudinal and multi-modal risk refinement approaches, modeled on human physicians, that can accomodate variable and limited prior context into risk assessment frameworks. arXiv
- Develop foundation models to learn generalized representations from multi-modal (imaging + clinical variables) medical data
- Self-supervised representation learning approach to identify increases in risk over time for longitudinal imaging ISMRM Proc
- Leverage representation learning approaches to model a radiologists assessment of prostate MR images from data in which radiologists are confident in their assessment of risk for clinically significant prostate cancer, and use it to disambiguate the equivocal PI-RADS 3 assessments, and avoid unnecessary biopsies in general. Paper
- Leverage underlying tissue-specific information in multi-contrast MR images to learn representations of local regions in an image such that regions belonging to similar tissue types generate similar representations. This constained contrastive learning approach is used to pretrain deep learning models to reduce the amount of labeled data required for downstream segmentation tasks in MRI. Paper
- Leverage shared information in multi-contrast MR images to synthesize Gd-enhanced contrast MR images from non-contrast enhanced MR images that are routinely acquired in the clinic (T1-weighted, T2-weighted, FLAIR). ISMRM Proc
- Synthesize white matter nulled MPRAGE images from conventional T1-weighted MPRAGE images to improve thalamic nuclei segmentation. Paper
- Learning spatio-temporal characteristics in multi-contrast MR images with self-supervision: An application to accelerating quantitative T2 mapping. ISMRM Proc
- Stacked generalization ensemble of 3D orthogonal deep learning models for white matter hyperintensity segmentation in 3D T2-FLAIR images. Paper
- 2D and 3D deep learning models for multi-organ segmentation in body MRI arXiv
- Detection of subtle globe injuries in CT Orbits images with automated segmentation of globes and volume assessment Paper
- Liver fibrosis staging with texture analysis in delayed-phase Gadolinium-enhanced T1-weighted MRI of the liver. ISMRM Proc
