Search Results for author: Ross T. Whitaker

Found 6 papers, 0 papers with code

Learning Deep Features for Shape Correspondence with Domain Invariance

no code implementations21 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.

Anatomy Unsupervised Domain Adaptation

GENs: Generative Encoding Networks

no code implementations28 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}.

Unsupervised Shape Normality Metric for Severity Quantification

no code implementations18 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.


A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration

no code implementations16 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.

Image Registration

Clustering With Pairwise Relationships: A Generative Approach

no code implementations6 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.

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