no code implementations • 21 Feb 2021 • Praful Agrawal, Ross T. Whitaker, Shireen Y. Elhabian
Further, unsupervised learning is demonstrated to learn complex anatomy features using the supervised domain adaptation from features learned on simpler anatomy.
no code implementations • 28 Oct 2020 • Surojit Saha, Shireen Elhabian, Ross T. Whitaker
Using the proposed method, we enforce the latent representation of an autoencoder to match a target distribution in a learning framework that we call a {\em generative encoding network}.
no code implementations • 18 Jul 2020 • Wenzheng Tao, Riddhish Bhalodia, Erin Anstadt, Ladislav Kavan, Ross T. Whitaker, Jesse A. Goldstein
The severity of an anatomical deformity often serves as a determinant in the clinical management of patients.
no code implementations • 16 Aug 2019 • Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan, Ross T. Whitaker
We propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration.
no code implementations • 28 Sep 2018 • Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan, Ross T. Whitaker
Statistical shape modeling is an important tool to characterize variation in anatomical morphology.
no code implementations • 6 May 2018 • Yen-Yun Yu, Shireen Y. Elhabian, Ross T. Whitaker
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense.