Publications (15)
DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams +3
Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. SSM requires consistent shape representation across sa…
Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science
Ross Whitaker, William Thompson, James Berger +7
Modern science, technology, and politics are all permeated by data that comes from people, measurements, or computational processes. While this data is often incomplete, corrupt, o…
On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application
Anupama Goparaju, Ibolya Csecs, Alan Morris +4
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical…
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation
Riddhish Bhalodia, Anupama Goparaju, Tim Sodergren +6
Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimatio…
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies
Surojit Saha, Ross Whitaker
Automated interpretation of seismic images using deep learning methods is challenging because of the limited availability of training data. Few-shot learning is a suitable learning…
SetGAN: Improving the stability and diversity of generative models through a permutation invariant architecture
Alessandro Ferrero, Shireen Elhabian, Ross Whitaker
Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimensional data. However, their training instability is a well-known hindrance to co…