Quantifying physiological variability and improving reproducibility in 4D‐flow MRI cerebrovascular measurements with self‐supervised deep learning
Sign Up to like & getrecommendations! Published in 2025 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.30634
Abstract: To assess the efficacy of self‐supervised deep learning (DL) denoising in reducing measurement variability in 4D‐Flow MRI, and to clarify the contributions of physiological variation to cerebrovascular hemodynamics. read more here.
Keywords: deep learning; self supervised; flow mri; variability ... See more keywords
Supervised deep hashing for image content security
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DOI: 10.1007/s11042-017-5433-z
Abstract: Due to the fast growth of image data on the web, it is necessary to ensure the content security of uploaded images. One of the fundamental problems behind this need is retrieving relevant images from… read more here.
Keywords: supervised deep; deep hashing; image; content security ... See more keywords
A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data
Sign Up to like & getrecommendations! Published in 2018 at "Computer methods and programs in biomedicine"
DOI: 10.1016/j.cmpb.2018.10.004
Abstract: BACKGROUND AND OBJECTIVE Cancer has become a complex health problem due to its high mortality. Over the past few decades, with the rapid development of the high-throughput sequencing technology and the application of various machine… read more here.
Keywords: semi supervised; supervised deep; seq data; cancer ... See more keywords
Supervised deep convolutional generative adversarial networks
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.03.125
Abstract: Abstract Generative adversarial networks (GANs) are one of the most important generative network models. Using real samples, the GAN generates fake samples from the noise given as input to the network. This popular network model,… read more here.
Keywords: generative adversarial; deep convolutional; adversarial networks; category label ... See more keywords
A semi-supervised deep-learning approach for automatic crystal structure classification
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Applied Crystallography"
DOI: 10.1107/s1600576722006069
Abstract: A semi-supervised model to predict crystal structures from powder neutron diffraction patterns has been developed. The models have higher accuracies than current approaches while covering more space groups. read more here.
Keywords: semi; semi supervised; approach automatic; deep learning ... See more keywords
Semi-Supervised Deep Fuzzy C-Mean Clustering for Imbalanced Multi-Class Classification
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2901860
Abstract: Semi-supervised learning has been successfully connected in the research fields of machine learning such as data mining and dynamic data analysis. Imbalance class learning is one of the most challenging issues for classification. In recent… read more here.
Keywords: imbalance; semi supervised; class; multi class ... See more keywords
SSDH: Semi-Supervised Deep Hashing for Large Scale Image Retrieval
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2017.2771332
Abstract: Hashing methods have been widely used for efficient similarity retrieval on large scale image database. Traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted… read more here.
Keywords: deep hashing; similarity; semi supervised; hashing methods ... See more keywords
A Semi-supervised Deep Transfer Learning Approach for Rolling-Element Bearing Remaining Useful Life Prediction
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Energy Conversion"
DOI: 10.1109/tec.2021.3116423
Abstract: Deep learning techniques have recently brought many improvements in the field of neural network training, especially for prognosis and health management. The success of such an intelligent health assessment model depends not only on the… read more here.
Keywords: transfer learning; semi supervised; supervised deep; deep transfer ... See more keywords
Semi-supervised Deep Domain Adaptation via Coupled Neural Networks
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2018.2851067
Abstract: Domain adaptation is a promising technique when addressing limited or no labeled target data by borrowing well-labeled knowledge from the auxiliary source data. Recently, researchers have exploited multi-layer structures for discriminative feature learning to reduce… read more here.
Keywords: semi supervised; supervised deep; deep domain; domain adaptation ... See more keywords
Supervised Deep Feature Embedding With Handcrafted Feature
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2019.2901407
Abstract: Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can… read more here.
Keywords: deep feature; feature; image; handcrafted feature ... See more keywords
SDAC-DA: Semi-Supervised Deep Attributed Clustering Using Dual Autoencoder
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2024.3389049
Abstract: Attributed graph clustering aims to group nodes into disjoint categories using deep learning to represent node embeddings and has shown promising performance across various applications. However, two main challenges hinder further performance improvement. First, reliance… read more here.
Keywords: autoencoder; deep attributed; semi supervised; attributed clustering ... See more keywords