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SHRiMP: Accurate Mapping of Short Color-space Reads
TLDR
It is demonstrated that SHRiMP can accurately map reads to this highly polymorphic genome, while confirming high heterozygosity of C. savignyi in this second individual.
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
TLDR
VoxelMorph promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications and demonstrates that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster.
An Unsupervised Learning Model for Deformable Medical Image Registration
TLDR
The proposed method uses a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field, and demonstrates registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice.
Synthesizing Images of Humans in Unseen Poses
TLDR
A modular generative neural network is presented that synthesizes unseen poses using training pairs of images and poses taken from human action videos, separates a scene into different body part and background layers, moves body parts to new locations and refines their appearances, and composites the new foreground with a hole-filled background.
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
TLDR
This paper presents a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs) and results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates.
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation
TLDR
This work learns a model of transformations from the images, and uses the model along with the labeled example to synthesize additional labeled examples, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures.
Gaussian Process Prior Variational Autoencoders
TLDR
A new model is introduced, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), which aims to combine the power of VAEs with the ability to model correlations afforded by GP priors, and leverages structure in the covariance matrix to achieve efficient inference in this new class of models.
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
TLDR
A probabilistic generative model is presented and an unsupervised learning-based inference algorithm is derived that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).
Interactive Whole-Heart Segmentation in Congenital Heart Disease
TLDR
This work presents an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease, and shows that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short- axis slices.
Learning-based Optimization of the Under-sampling Pattern in MRI
TLDR
The proposed method, which the authors call LOUPE (Learning-based Optimization of the Under-sampling PattErn), was implemented by modifying a U-Net, a widely-used convolutional neural network architecture, that is append with the forward model that encodes the under-sampled process.
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