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Found 13 papers, 2 papers with code
LightHGNN: Distilling Hypergraph Neural Networks into MLPs for $100\times$ Faster Inference
Experiments on eight hypergraph datasets demonstrate that even without hypergraph dependency, the proposed LightHGNNs can still achieve competitive or even better performance than HGNNs and outperform vanilla MLPs by $16. 3$ on average.
Deep Unfolding Network with Spatial Alignment for multi-modal MRI reconstruction
In this paper, we construct a novel Deep Unfolding Network with Spatial Alignment, termed DUN-SA, to appropriately embed the spatial alignment task into the reconstruction process.
HNS: An Efficient Hermite Neural Solver for Solving Time-Fractional Partial Differential Equations
However, we have discovered that neural network solvers based on L1 interpolation approximation are unable to fully exploit the benefits of neural networks, and the accuracy of these models is constrained to interpolation errors.
Hypergraph Isomorphism Computation
The isomorphism problem is a fundamental problem in network analysis, which involves capturing both low-order and high-order structural information.
Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers with Partially Annotated Ultrasound Images
To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to enhance diagnostic accuracy of the ultrasound-based CAD for breast cancers.
Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification
The key idea of MEGT is to adopt two independent Efficient Graph-based Transformer (EGT) branches to process the low-resolution and high-resolution patch embeddings (i. e., tokens in a Transformer) of WSIs, respectively, and then fuse these tokens via a multi-scale feature fusion module (MFFM).
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction
Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine.
Fast MRI Reconstruction via Edge Attention
To solve this problem, we propose a lightweight and accurate Edge Attention MRI Reconstruction Network (EAMRI) to reconstruct images with edge guidance.
Domain Adaptive Semantic Segmentation by Optimal Transport
Second, we utilize OT to achieve a more robust alignment of source and target domains in output space, where the OT plan defines a well attention mechanism to improve the adaptation of the model.
Sliding at first order: Higher-order momentum distributions for discontinuous image registration
In this paper, we propose a new approach to deformable image registration that captures sliding motions.
Deep Hypergraph Structure Learning
To address this issue, we design a general paradigm of deep hypergraph structure learning, namely DeepHGSL, to optimize the hypergraph structure for hypergraph-based representation learning.
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images
In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models.
Groupwise Registration via Graph Shrinkage on the Image Manifold
Specifically, we first use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images).
