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Found 7 papers, 1 papers with code
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.
