Classifying cancer pathology reports with hierarchical self-attention networks
Sign Up to like & getrecommendations! Published in 2019 at "Artificial intelligence in medicine"
DOI: 10.1016/j.artmed.2019.101726
Abstract: We introduce a deep learning architecture, hierarchical self-attention networks (HiSANs), designed for classifying pathology reports and show how its unique architecture leads to a new state-of-the-art in accuracy, faster training, and clear interpretability. We evaluate… read more here.
Keywords: pathology reports; self attention; pathology; attention networks ... See more keywords
Are primary and secondary types of brain anomalies exclusive factors affecting the attention networks in individuals with spina bifida?
Sign Up to like & getrecommendations! Published in 2019 at "Neuropsychology"
DOI: 10.1037/neu0000581
Abstract: OBJECTIVE Individuals with spina bifida myelomeningocele (SBM) frequently exhibit cognitive impairments on tasks mediated by brain regions involved in the posterior attention network. Although such deficits have been historically assumed to result from primary and… read more here.
Keywords: individuals spina; attention; attention networks; primary secondary ... See more keywords
Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes.
Sign Up to like & getrecommendations! Published in 2021 at "Nature methods"
DOI: 10.1038/s41592-021-01155-x
Abstract: We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with… read more here.
Keywords: resolution; image; microscopy; attention networks ... See more keywords
Spatiotemporal Dynamics of Attention Networks Revealed by Representational Similarity Analysis of EEG and fMRI
Sign Up to like & getrecommendations! Published in 2018 at "Cerebral Cortex"
DOI: 10.1093/cercor/bhw389
Abstract: The fronto-parietal attention networks have been extensively studied with functional magnetic resonance imaging (fMRI), but spatiotemporal dynamics of these networks are not well understood. We measured event-related potentials (ERPs) with electroencephalography (EEG) and collected fMRI… read more here.
Keywords: analysis; attention; representational similarity; similarity analysis ... See more keywords
FPGAN: An FPGA Accelerator for Graph Attention Networks With Software and Hardware Co-Optimization
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3023946
Abstract: The Graph Attention Networks (GATs) exhibit outstanding performance in multiple authoritative node classification benchmark tests (including transductive and inductive). The purpose of this research is to implement an FPGA-based accelerator called FPGAN for graph attention… read more here.
Keywords: graph attention; software hardware; hardware optimization; attention networks ... See more keywords
Semantic Representations With Attention Networks for Boosting Image Captioning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3268744
Abstract: Image captioning has shown encouraging outcomes with Transformer-based architectures that typically use attention-based methods to establish semantic associations between objects in an image for caption prediction. Nevertheless, when appearance features of objects in an image… read more here.
Keywords: attention networks; representations attention; attention; semantic representations ... See more keywords
Predicting Mirna-Disease Associations Based on Neighbor Selection Graph Attention Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2022.3204726
Abstract: Numerous experiments have shown that the occurrence of complex human diseases is often accompanied by abnormal expression of microRNA (miRNA). Identifying the associations between miRNAs and diseases is of great significance in the development of… read more here.
Keywords: attention networks; disease; predicting mirna; neighbor selection ... See more keywords
TC-GATN: Temporal Causal Graph Attention Networks With Nonlinear Paradigm for Multivariate Time-Series Forecasting in Industrial Processes
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3211330
Abstract: Multivariate time-series (MTS) forecasting plays an important role in industrial process monitoring, control, and optimization. Usually, hierarchical interactive behaviors among industrial MTS have formed complex nonlinear causal characteristics, which greatly hinders the applications of the… read more here.
Keywords: attention networks; causal; attention; graph attention ... See more keywords
A Topic Detection Method Based on Word-attention Networks
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Data and Information Science"
DOI: 10.2478/jdis-2021-0032
Abstract: Abstract Purpose We proposed a method to represent scientific papers by a complex network, which combines the approaches of neural and complex networks. Design/methodology/approach Its novelty is representing a paper by a word branch, which… read more here.
Keywords: word attention; method; word; attention ... See more keywords
Hyper and hypo attention networks activations affect social development in children with autism spectrum disorder
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Human Neuroscience"
DOI: 10.3389/fnhum.2022.902041
Abstract: Children with autism spectrum disorder (ASD) experience a range of social and non-social attention deficits. To date, most studies assessed the neurological framework or discrete behavioral traits related to one attention network, leaving a gap… read more here.
Keywords: attention networks; networks activations; autism spectrum; spectrum disorder ... See more keywords
Wild Mushroom Classification Based on Improved MobileViT Deep Learning
Sign Up to like & getrecommendations! Published in 2023 at "Applied Sciences"
DOI: 10.3390/app13084680
Abstract: Wild mushrooms are not only tasty but also rich in nutritional value, but it is difficult for non-specialists to distinguish poisonous wild mushrooms accurately. Given the frequent occurrence of wild mushroom poisoning, we propose a… read more here.
Keywords: attention; attention networks; mushroom classification; wild mushroom ... See more keywords