Results 91 to 100 of about 792,416 (284)
Going Deeper with Dense Connectedly Convolutional Neural Networks for Multispectral Pansharpening
Remote Sensing, 2019 In recent years, convolutional neural networks (CNNs) have shown promising performance in the field of multispectral (MS) and panchromatic (PAN) image fusion (MS pansharpening).
Dong Wang +4 more
doaj +1 more source
Tumor mutational burden as a determinant of metastatic dissemination patterns
Molecular Oncology, EarlyView.This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal +4 more
wiley +1 more source
Multi-Scale Residual Convolutional Neural Network for Haze Removal of Remote Sensing Images
Remote Sensing, 2018 Haze removal is a pre-processing step that operates on at-sensor radiance data prior to the physically based image correction step to enhance hazy imagery visually.
Hou Jiang, Ning Lu
doaj +1 more source
Molecular Oncology, EarlyView.To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley +1 more source
A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification
IEEE Access, 2020 Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of ...
Javier Naranjo-Alcazar +5 more
doaj +1 more source
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
IEEE Access, 2020 Semantic segmentation is one of the most commonly used techniques for road scene understanding. Recently developed deep learning-based semantic segmentation networks are typically based on the encoder-decoder structure and have made great progress in ...
Jee-Young Sun +2 more
doaj +1 more source
Dictionary Learning of Convolved Signals [PDF]
, 2013 Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation of them.
Barchiesi, Daniele, Plumbley, Mark
core +1 more source
Rethinking plastic waste: innovations in enzymatic breakdown of oil‐based polyesters and bioplastics
FEBS Open Bio, EarlyView.Plastic pollution remains a critical environmental challenge, and current mechanical and chemical recycling methods are insufficient to achieve a fully circular economy. This review highlights recent breakthroughs in the enzymatic depolymerization of both oil‐derived polyesters and bioplastics, including high‐throughput protein engineering, de novo ...
Elena Rosini +2 more
wiley +1 more source
IEEE AccessEfficient diagnosis, treatment planning, and patient care requires that brain tumors in MRI images must be accurately segmented and classified. Many deep learning practices are used to perform these tasks.
Muhammad Sami Ullah +6 more
doaj +1 more source