Search Results for author: Riddhish Bhalodia

Found 14 papers, 1 papers with code

RENs: Relevance Encoding Networks

no code implementations25 May 2022 Krithika Iyer, Riddhish Bhalodia, Shireen Elhabian

With extensive experimentation on synthetic and public image datasets, we show that the proposed model learns the relevant latent bottleneck dimensionality without compromising the representation and generation quality of the samples.

Disentanglement

Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model

no code implementations10 Jan 2022 Wenzheng Tao, Riddhish Bhalodia, Shireen Elhabian

The proposed joint model serves a dual purpose; the world correspondences can directly be used for shape analysis applications, circumventing the heavy pre-processing and segmentation involved in traditional PDM models.

Anatomy Segmentation

Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor

1 code implementation13 Nov 2021 Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan, Ross Whitaker

We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well.

Anatomy Dimensionality Reduction +4

DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models

no code implementations14 Oct 2021 Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker

Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images.

Anatomy Data Augmentation

Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports

no code implementations6 Oct 2021 Riddhish Bhalodia, Ali Hatamizadeh, Leo Tam, Ziyue Xu, Xiaosong Wang, Evrim Turkbey, Daguang Xu

Both the classification and localization are trained in conjunction and once trained, the model can be utilized for both the localization and characterization of pneumonia using only the input image.

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.

Management

Uncertain-DeepSSM: From Images to Probabilistic Shape Models

no code implementations13 Jul 2020 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 segmentation, shape registration, and the optimization of population-level shape representations.

Anatomy

Self-Supervised Discovery of Anatomical Shape Landmarks

no code implementations13 Jun 2020 Riddhish Bhalodia, Ladislav Kavan, Ross Whitaker

In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis.

Image Registration

dpVAEs: Fixing Sample Generation for Regularized VAEs

no code implementations24 Nov 2019 Riddhish Bhalodia, Iain Lee, Shireen Elhabian

This decoupling enables the use of VAE regularizers on the representation space without impacting the distribution used for sample generation, and thereby reaping the representation learning benefits of the regularizations without sacrificing the sample generation.

Representation Learning

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.

Computational Efficiency Image Registration

Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation

no code implementations30 Sep 2018 Riddhish Bhalodia, Anupama Goparaju, Tim Sodergren, Alan Morris, Evgueni Kholmovski, Nassir Marrouche, Joshua Cates, Ross Whitaker, Shireen Elhabian

In this paper, we propose a machine learning approach that uses deep networks to estimate AF recurrence by predicting shape descriptors directly from MRI images, with NO image pre-processing involved.

Anatomy Atrial Fibrillation Recurrence Estimation +4

Cannot find the paper you are looking for? You can Submit a new open access paper.