Search Results for author:
Found 10 papers, 1 papers with code
Modality Exchange Network for Retinogeniculate Visual Pathway Segmentation
Secondly, we propose a cross-fusion module that further enhances the fusion of information between the two modalities.
LESEN: Label-Efficient deep learning for Multi-parametric MRI-based Visual Pathway Segmentation
Recent research has shown the potential of deep learning in multi-parametric MRI-based visual pathway (VP) segmentation.
Blind2Sound: Self-Supervised Image Denoising without Residual Noise
Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task.
Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic.
Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots
Self-supervised denoisers, which learn only from single noisy images, solve the data collection problem.
Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images
The TSA interpolation module aggregates temporal contexts and then adaptively samples the spatial-related features with the proposed residual spatial adaptive block.
Non-iterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissue
In this paper, we propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images, which is a key component in volume reconstruction of serial sections of biological tissue.
DAN: A Deformation-Aware Network for Consecutive Biomedical Image Interpolation
The continuity of biological tissue between consecutive biomedical images makes it possible for the video interpolation algorithm, to recover large area defects and tears that are common in biomedical images.
EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction
To address this issue, an evolutionary attention-based LSTM training with competitive random search is proposed for multivariate time series prediction.
Hierarchical Spatial Transformer Network
Computer vision researchers have been expecting that neural networks have spatial transformation ability to eliminate the interference caused by geometric distortion for a long time.
