no code implementations • 25 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.
no code implementations • 10 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.
1 code implementation • 13 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.
no code implementations • 14 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.
no code implementations • 6 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.
no code implementations • 18 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.
no code implementations • 13 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.
no code implementations • 13 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.
no code implementations • 24 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.
no code implementations • 16 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.
no code implementations • 13 Jun 2019 • Riddhish Bhalodia, Shireen Elhabian, Ladislav Kavan, Ross Whitaker
Deep networks are an integral part of the current machine learning paradigm.
no code implementations • 6 Mar 2019 • Tim Sodergren, Riddhish Bhalodia, Ross Whitaker, Joshua Cates, Nassir Marrouche, Shireen Elhabian
Here, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local intensity and global shape priors.
no code implementations • 30 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.
no code implementations • 28 Sep 2018 • Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan, Ross T. Whitaker
Statistical shape modeling is an important tool to characterize variation in anatomical morphology.