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Found 15 papers, 8 papers with code
STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification
Multimodal image-tabular learning is gaining attention, yet it faces challenges due to limited labeled data.
TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data
Moreover, TIP proposes a versatile tabular encoder tailored for incomplete, heterogeneous tabular data and a multimodal interaction module for inter-modality representation learning.
CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases.
Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta
We built a data-driven generative model of 4D aortic velocity profiles, suitable to be used in computational studies of blood flow.
Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration
Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods.
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation
Optimising the analysis of cardiac structure and function requires accurate 3D representations of shape and motion.
Learning a Model-Driven Variational Network for Deformable Image Registration
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network
In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure.
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data.
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space.
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology.
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images.
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation.
Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension
In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH).
Learning-Based Quality Control for Cardiac MR Images
The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e. g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.
