309 citations · 1k across the 22 of their papers we have counts for
7 papers · 1 filter
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor
Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan +1
In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often…
Capturing Detailed Deformations of Moving Human Bodies
He Chen, Hyojoon Park, Kutay Macit +1
We present a new method to capture detailed human motion, sampling more than 1000 unique points on the body. Our method outputs highly accurate 4D (spatio-temporal) point coordinat…
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,…