Results 61 to 70 of about 324,009 (309)
SACNet: Shuffling atrous convolutional U‐Net for medical image segmentation
IET Image Processing, 2023 Medical images exhibit multi‐granularity and high obscurity along boundaries. As representative work, the U‐Net and its variants exhibit two shortcomings on medical image segmentation: (a) they expand the range of reception fields by applying addition or
Shaofan Wang +3 more
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Convolutional deep rectifier neural nets for phone recognition [PDF]
Interspeech 2013, 2013 Rectifier neurons differ from standard ones only in that the sigmoid activation function is replaced by the rectifier function, max(0, x). Several recent studies suggest that rectifier units may be more suitable building units for deep nets. For example, we found that with deep rectifier networks one can attain a similar speech recognition performance ...
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective The cervical spinal cord (cSC) is highly relevant to clinical dysfunction in multiple sclerosis (MS) but remains understudied using quantitative magnetic resonance imaging (MRI). We assessed magnetization transfer ratio (MTR), a semi‐quantitative MRI measure sensitive to MS‐related tissue microstructural changes, in the cSC and its ...
Lisa Eunyoung Lee +26 more
wiley +1 more source
IET Computer Vision, 2022 Despite recent advances in 3‐D pose estimation of human hands, thanks to the advent of convolutional neural networks (CNNs) and depth cameras, this task is still far from being solved in uncontrolled setups.
Meysam Madadi +3 more
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Augmenting Paraphrase Generation with Syntax Information Using Graph Convolutional Networks
Entropy, 2021 Paraphrase generation is an important yet challenging task in natural language processing. Neural network-based approaches have achieved remarkable success in sequence-to-sequence learning.
Xiaoqiang Chi, Yang Xiang
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Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
, 2018 Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive task. As it has to compute million of parameters, it results to huge memory consumption.
Kamran, Sharif Amit, Sabbir, Ali Shihab
core +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
Cells, 2020 Over a decade ago, the formation of neutrophil extracellular traps (NETs) was described as a novel mechanism employed by neutrophils to tackle infections.
Aneta Manda-Handzlik +4 more
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Generalizable and efficient cross‐domain person re‐identification model using deep metric learning
IET Computer Vision, 2023 Most of the successful person re‐ID models conduct supervised training and need a large number of training data. These models fail to generalise well on unseen unlabelled testing sets.
Saba Sadat Faghih Imani +2 more
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Structurally Colored Physically Unclonable Functions with Ultra‐Rich and Stable Encoding Capacity
Advanced Functional Materials, Volume 35, Issue 12, March 18, 2025.This study reports a design strategy for generating bright‐field resolvable physically unclonable functions with extremely rich encoding capacity coupled with outstanding thermal and chemical stability. The optical response emerges from thickness‐dependent structural color formation in ZnO features, which are fabricated by physical vapor deposition ...
Abidin Esidir +8 more
wiley +1 more source