6 citations · 10 across the 7 of their papers we have counts for
11 papers
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…
Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports
Riddhish Bhalodia, Ali Hatamizadeh, Leo Tam +4
Localization and characterization of diseases like pneumonia are primary steps in a clinical pipeline, facilitating detailed clinical diagnosis and subsequent treatment planning. A…
Uncertain-DeepSSM: From Images to Probabilistic Shape Models
Jadie Adams, Riddhish Bhalodia, Shireen Elhabian
Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmenta…
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…
dpVAEs: Fixing Sample Generation for Regularized VAEs
Riddhish Bhalodia, Iain Lee, Shireen Elhabian
Unsupervised representation learning via generative modeling is a staple to many computer vision applications in the absence of labeled data. Variational Autoencoders (VAEs) are po…
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,…
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation
Tim Sodergren, Riddhish Bhalodia, Ross Whitaker +3
Difficult image segmentation problems, for instance left atrium MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Noneth…
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…
DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images
Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan +1
Statistical shape modeling is an important tool to characterize variation in anatomical morphology. Typical shapes of interest are measured using 3D imaging and a subsequent pipeli…