4 citations · 6 across the 11 of their papers we have counts for
9 papers · 1 filter
SurfR: Surface Reconstruction with Multi-scale Attention
Siddhant Ranade, Gonçalo Dias Pais, Ross Tyler Whitaker +3
We propose a fast and accurate surface reconstruction algorithm for unorganized point clouds using an implicit representation. Recent learning methods are either single-object repr…
Learning Deep Features for Shape Correspondence with Domain Invariance
Praful Agrawal, Ross T. Whitaker, Shireen Y. Elhabian
Correspondence-based shape models are key to various medical imaging applications that rely on a statistical analysis of anatomies. Such shape models are expected to represent cons…
Unsupervised Shape Normality Metric for Severity Quantification
Wenzheng Tao, Riddhish Bhalodia, Erin Anstadt +3
This work describes an unsupervised method to objectively quantify the abnormality of general anatomical shapes. The severity of an anatomical deformity often serves as a determina…
Self-Supervised Discovery of Anatomical Shape Landmarks
Riddhish Bhalodia, Ladislav Kavan, Ross Whitaker
Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small n…
A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration
Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan +1
Spatial transformations are enablers in a variety of medical image analysis applications that entail aligning images to a common coordinate systems. Population analysis of such tra…
CoopSubNet: Cooperating Subnetwork for Data-Driven Regularization of Deep Networks under Limited Training Budgets
Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan +1
Deep networks are an integral part of the current machine learning paradigm. Their inherent ability to learn complex functional mappings between data and various target variables,…