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Found 17 papers, 5 papers with code
Vestibular schwannoma growth prediction from longitudinal MRI by time conditioned neural fields
In the proposed method, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation
Deep learning (DL)-based methods have achieved state-of-the-art performance for a wide range of medical image segmentation tasks.
Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction
Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications.
Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI
To address the challenge, we propose a novel motion correction framework based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, and we integrate the groupwise CNN-based registration backbone within the rPCA framework.
CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation
In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation.
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness
The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization.
LON-GNN: Spectral GNNs with Learnable Orthonormal Basis
In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node-level tasks.
Efficient Bayesian Uncertainty Estimation for nnU-Net
Different from previous baseline methods such as Monte Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does not require a variational architecture and keeps the original nnU-Net architecture intact, thereby preserving its excellent performance and ease of use.
Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification
Graph-based semi-supervised learning, which can exploit the connectivity relationship between labeled and unlabeled data, has been shown to outperform the state-of-the-art in many artificial intelligence applications.
Deep Recursive Embedding for High-Dimensional Data
Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value.
SpaceMAP: Visualizing Any Data in 2-dimension by Space Expansion
Dimensionality reduction (DR) and visualization of high-dimensional data is of theoretical and practical value in machine learning and related fields.
Deep Recursive Embedding for High-Dimensional Data
t-distributed stochastic neighbor embedding (t-SNE) is a well-established visualization method for complex high-dimensional data.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN
In this work, we proposed a generic framework to address this problem, consisting of (1) an unpaired generative adversarial network (GAN) for vendor-adaptation, and (2) a Unet for object segmentation.
Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion
Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients.
