Results 91 to 100 of about 7,799,574 (379)
A Point Set Generation Network for 3D Object Reconstruction from a Single Image [PDF]
Generation of 3D data by deep neural network has been attracting increasing attention in the research community. The majority of extant works resort to regular representations such as volumetric grids or collection of images, however, these ...
Haoqiang Fan, Hao Su, L. Guibas
semanticscholar +1 more source
The authors analyzed the spatial distributions of gene and metabolite profiles in cervical cancer through spatial transcriptomic and spatially resolved metabolomic techniques. Pivotal genes and metabolites within these cases were then identified and validated.
Lixiu Xu+3 more
wiley +1 more source
PURPOSE: We develop a practical, iterative algorithm for image-reconstruction in under-sampled tomographic systems, such as digital breast tomosynthesis (DBT). METHOD: The algorithm controls image regularity by minimizing the image total $p$-variation (
Candès+21 more
core +1 more source
Classification of acute myeloid leukemia based on multi‐omics and prognosis prediction value
The Unsupervised AML Multi‐Omics Classification System (UAMOCS) integrates genomic, methylation, and transcriptomic data to categorize AML patients into three subtypes (UAMOCS1‐3). This classification reveals clinical relevance, highlighting immune and chromosomal characteristics, prognosis, and therapeutic vulnerabilities.
Yang Song+13 more
wiley +1 more source
We report the first experimental test of an analytic image reconstruction algorithm for optical tomography with large data sets. Using a continuous-wave optical tomography system with 10^8 source-detector pairs, we demonstrate the reconstruction of an ...
Colak+13 more
core +1 more source
Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity [PDF]
Reconstructing perceived images from human brain activity monitored by functional magnetic resonance imaging (fMRI) is hard, especially for natural images. Existing methods often result in blurry and unintelligible reconstructions with low fidelity. In this study, we present a novel approach for enhanced image reconstruction, in which existing methods ...
arxiv
Deep Learning CT Image Reconstruction in Clinical Practice
Background Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services.
Clemens Arndt+5 more
semanticscholar +1 more source
B‐cell chronic lymphocytic leukemia (B‐CLL) and monoclonal B‐cell lymphocytosis (MBL) show altered proteomes and phosphoproteomes, analyzed using mass spectrometry, protein microarrays, and western blotting. Identifying 2970 proteins and 316 phosphoproteins, including 55 novel phosphopeptides, we reveal BCR and NF‐kβ/STAT3 signaling in disease ...
Paula Díez+17 more
wiley +1 more source
Super-resolution reconstruction for a single image based on self-similarity and compressed sensing
Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse ...
Qiang Yang, Huajun Wang
doaj +1 more source
Spatial Domain Terahertz Image Reconstruction Based on Dual Sparsity Constraints
Terahertz time domain spectroscopy imaging systems suffer from the problems of long image acquisition time and massive data processing. Reducing the sampling rate will lead to the degradation of the imaging reconstruction quality.
Xiaozhen Ren, Yuying Jiang
doaj +1 more source