4 papers
PRISM: A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
Yining Jiao, Sreekalyani Bhamidi, Carlton Jude Zdanski +8
Understanding how anatomical shapes evolve in response to developmental covariates - and quantifying their spatially varying uncertainties - is critical in healthcare research. Exi…
LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations
Yining Jiao, Sreekalyani Bhamidi, Huaizhi Qu +11
The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provid…
NeuralOCT: Airway OCT Analysis via Neural Fields
Yining Jiao, Amy Oldenburg, Yinghan Xu +4
Optical coherence tomography (OCT) is a popular modality in ophthalmology and is also used intravascularly. Our interest in this work is OCT in the context of airway abnormalities…
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Yining Jiao, Carlton Zdanski, Julia Kimbell +8
Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, an…