Search Results for author:
Found 26 papers, 7 papers with code
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images
To address this, we propose a two-stage deep-learning approach for automated LGE-MRI image diagnostic quality assessment.
Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models
The training is performed in multiple scales, and each scale utilizes the output from the previous scale.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images
However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models.
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology
In this study, we compare the performance of gland and cell segmentation tasks with histopathology domain-specific and non-domain-specific (real-world images) pretrained weights.
Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud
We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds.
Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy
Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population.
Fully Bayesian VIB-DeepSSM
We derive a fully Bayesian VIB formulation and demonstrate the efficacy of two scalable implementation approaches: concrete dropout and batch ensemble.
Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching
Nonetheless, they often fail to generalize when there is a significant domain (i. e., distributional) shift between the training (i. e., source) data and the dataset(s) encountered when deployed (i. e., target), necessitating manual annotations for the target data to achieve acceptable performance.
Can point cloud networks learn statistical shape models of anatomies?
Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies.
Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach
Clinical investigations of anatomy's structural changes over time could greatly benefit from population-level quantification of shape, or spatiotemporal statistic shape modeling (SSM).
Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries
This paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that capture morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population.
RENs: Relevance Encoding Networks
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.
From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach
This constraint restricts the learning task to solely estimating pre-defined shape descriptors from 3D images and imposes a linear relationship between this shape representation and the output (i. e., shape) space.
Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model
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.
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape Descriptor
We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well.
DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models
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.
GENs: Generative Encoding Networks
Using the proposed method, we enforce the latent representation of an autoencoder to match a target distribution in a learning framework that we call a {\em generative encoding network}.
Uncertain-DeepSSM: From Images to Probabilistic Shape Models
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.
dpVAEs: Fixing Sample Generation for Regularized VAEs
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.
SetGAN: Improving the stability and diversity of generative models through a permutation invariant architecture
Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimensional data.
CoopSubNet: Cooperating Subnetwork for Data-Driven Regularization of Deep Networks under Limited Training Budgets
Deep networks are an integral part of the current machine learning paradigm.
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation
Here, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local intensity and global shape priors.
On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation
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.
ShapeOdds: Variational Bayesian Learning of Generative Shape Models
Learning generative shape models from grid-structured representations, aka silhouettes, is usually hindered by (1) data likelihoods with intractable marginals and posteriors, (2) high-dimensional shape spaces with limited training samples (and the associated risk of overfitting), and (3) estimation of hyperparameters relating to model complexity that often entails computationally expensive grid searches.
