Scalable spatial transcriptomics through computational array reconstruction
We present an imaging-free approach that reconstructs spatial barcode locations using molecular diffusion and dimensionality reduction. Validated against ground truth imaging, our method...
MMR-Mamba: Multi-modal MRI reconstruction with Mamba and spatial
We present the MMR-Mamba framework, which effectively integrates information through the TCM module in the spatial domain and the SFF module in the frequency domain, along with
INRSCRECON: ENHANCING 3D SPATIAL TRANSCRIP
ized our understanding of the complex three-dimensional cellular landscapes of tissues. However, the accuracy of spa. ial expression profiles is often compromised by missing or distorted experimental
Che Sun, Xinjie Zhang, RuiGao, Xu Chen, Yuwei Wu, Yunde Jia
Specifically, we devise a dynamic-encoded module to extract micro-expression features for global facial motion, allowing it to leverage prior knowledge from abundant macro-expression data to mitigate the
Spatial-Frequency Multi-Scale Transformer for Deblurring and Shape
In this study, we propose a spatial-frequency multi-scale transformer (SFM-Transformer) to address this limitation by restoring both the spatial and frequency domain features of the native image.
MuST: multiple-modality structure transformation for single-cell
In gene reconstruction, we employ a graph decoder including a structure module (d/72, gene_dimension). We run all the experiments using a Ubuntu server and a single A100 GPU with 40
Spatial weight matrix in dimensionality reduction reconstruction for
Thus, in this study, we proposed a spatial weight matrix (SWM) with a dimensionality reduction for image reconstruction. The three-layer SWM contains the invariable information of the system, which
Scalable spatial transcriptomics through computational
We present an imaging-free approach that reconstructs spatial barcode locations using molecular diffusion and dimensionality reduction. Validated
High-resolution spatially resolved proteomics of complex tissues
We first demonstrated that our approach allows for spatial expression patterns to be reconstructed at the whole-tissue level using the mouse cerebellum. We further validated the ability
SSRM: Efficient spectral reconstruction Mamba with multiscale
In order to solve the existing problems, we propose an improved vision state-space model for the spectral reconstruction of RGB images. This model is a lightweight architecture with
The interpretable multimodal dimension reduction framework
SpaHDmap combines the trained U-Net-SpaHDmap model with two modules, the feature fusion module (encoder) and the data reconstruction module (decoder), to learn high-resolution
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