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Found 5 papers, 2 papers with code
Multi-View Vertebra Localization and Identification from CT Images
Additionally, we further propose a Sequence Loss to maintain the sequential structure embedded along the vertebrae.
Geometry-Aware Attenuation Field Learning for Sparse-View CBCT Reconstruction
This paper proposes a novel attenuation field encoder-decoder framework by first encoding the volumetric feature from multi-view X-ray projections, then decoding it into the desired attenuation field.
Hyperspectral image reconstruction for spectral camera based on ghost imaging via sparsity constraints using V-DUnet
When applying deep learning into GISC spectral camera, there are several challenges need to be solved: 1) how to deal with the large amount of 3D hyperspectral data, 2) how to reduce the influence caused by the uncertainty of the random reference measurements, 3) how to improve the reconstructed image quality as far as possible.
Deep learning tackles single-cell analysis A survey of deep learning for scRNA-seq analysis
Here we present a processing pipeline of single-cell RNA-seq data, survey a total of 25 DL algorithms and their applicability for a specific step in the processing pipeline.
Towards Top-Down Just Noticeable Difference Estimation of Natural Images
Instead of explicitly formulating and fusing different masking effects in a bottom-up way, the proposed JND estimation model dedicates to first predicting a critical perceptual lossless (CPL) counterpart of the original image and then calculating the difference map between the original image and the predicted CPL image as the JND map.
