Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Haitao Gan"'
Autor:
Yue Zhang, Haitao Gan, Furong Wang, Xinyao Cheng, Xiaoyan Wu, Jiaxuan Yan, Zhi Yang, Ran Zhou
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 2, Pp 3110-3128 (2024)
Carotid plaque classification from ultrasound images is crucial for predicting ischemic stroke risk. While deep learning has shown effectiveness, it heavily relies on substantial labeled datasets. Achieving high performance with limited labeled image
Externí odkaz:
https://doaj.org/article/3a3a9e0d881a48b1b8f13242d9ba9789
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 2, Pp 2212-2232 (2024)
Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However,
Externí odkaz:
https://doaj.org/article/d4e7bc5bfeae422d90083e005c447b7c
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 1, Pp 1554-1572 (2024)
Graph convolutional networks (GCN) have been widely utilized in Alzheimer's disease (AD) classification research due to its ability to automatically learn robust and powerful feature representations. Inter-patient relationships are effectively captur
Externí odkaz:
https://doaj.org/article/85a6a5a478ed4c54a66aef3ae4dd81f9
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 6, Pp 10610-10625 (2023)
The prediction of drug-target protein interaction (DTI) is a crucial task in the development of new drugs in modern medicine. Accurately identifying DTI through computer simulations can significantly reduce development time and costs. In recent years
Externí odkaz:
https://doaj.org/article/8b57e8f1009040fcb9ceb116c22cae9b
Autor:
Ran Zhou, Yanghan Ou, Xiaoyue Fang, M. Reza Azarpazhooh, Haitao Gan, Zhiwei Ye, J. David Spence, Xiangyang Xu, Aaron Fenster
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 2, Pp 1617-1636 (2023)
Carotid total plaque area (TPA) is an important contributing measurement to the evaluation of stroke risk. Deep learning provides an efficient method for ultrasound carotid plaque segmentation and TPA quantification. However, high performance of deep
Externí odkaz:
https://doaj.org/article/1e0817762f834d268048900c5f85a810
Publikováno v:
Mathematical Biosciences and Engineering, Vol 19, Iss 12, Pp 12677-12692 (2022)
In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the i
Externí odkaz:
https://doaj.org/article/035d6d1caab74e2bb0bd5c3fdc0ca736
Publikováno v:
Mathematical Biosciences and Engineering, Vol 19, Iss 10, Pp 10160-10175 (2022)
Ultrasound computed tomography (USCT) has been developed for breast tumor screening. The sound-speed modal of USCT can provide quantitative sound-speed values to help tumor diagnosis. Time-of-flight (TOF) is the critical input in sound-speed reconstr
Externí odkaz:
https://doaj.org/article/791a24144be04003b88ef4e938d1d85a
Publikováno v:
Mathematical Biosciences and Engineering, Vol 19, Iss 7, Pp 6907-6922 (2022)
Motor Imagery EEG (MI-EEG) classification plays an important role in different Brain-Computer Interface (BCI) systems. Recently, deep learning has been widely used in the MI-EEG classification tasks, however this technology requires a large number of
Externí odkaz:
https://doaj.org/article/ffcadfa8bbaa45e3a9018b320bebd848
Publikováno v:
Mathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7727-7742 (2021)
In the past few years, Safe Semi-Supervised Learning (S3L) has received considerable attentions in machine learning field. Different researchers have proposed many S3L methods for safe exploitation of risky unlabeled samples which result in performan
Externí odkaz:
https://doaj.org/article/0c8c3b2da2574d8788692f62e02d3bcb
Publikováno v:
IEEE Access, Vol 7, Pp 147857-147871 (2019)
The dehazing of images shot in fog is a hot spot in the study of computer vision. Unlike dehazing methods, which use an atmospheric scattering model, the method proposed here is based on fusion coding of contours and colors. It simulates the characte
Externí odkaz:
https://doaj.org/article/02021f87f5744597a39a1bd2f4718375