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Found 12 papers, 1 papers with code
Matching aggregate posteriors in the variational autoencoder
However, the VAE's known failure to match the aggregate posterior often results in \emph{pockets/holes} in the latent distribution (i. e., a failure to match the prior) and/or \emph{posterior collapse}, which is associated with a loss of information in the latent space.
Real-Time Idling Vehicles Detection using Combined Audio-Visual Deep Learning
In this paper we present a real-time, dynamic vehicle idling detection algorithm.
A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology
Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides.
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.
Self-Supervised Discovery of Anatomical Shape Landmarks
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.
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.
