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Haibo MEI, Kun YANG, Xinyu FAN • 物联网学报
(2021)
Fog radio access network (F-RAN) is suitable for Internet of things applications of national important industries, such as pipeline network monitoring in wide area.However, the performance of the F-RAN based on the territorial fog access po...
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Noritaka Fukunaga, Sho Sanami, Hiroya Kitasaka, Yuji Tsuzuki et al. • Reproductive Medicine and Biology
(2020)
Abstract Purpose To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. Methods An algorithm was developed by explicitly...
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Open Access (DOAJ)
DOI: 10.1002/rmb2.12331
Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza et al. • BMC Medical Informatics and Decision Making
(2025)
Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imba...
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Open Access (DOAJ)
DOI: 10.1186/s12911-025-02966-0
WANG Sili, ZHANG Ling, YANG Heng, LIU Wei • Nongye tushu qingbao xuebao
(2023)
[Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automa...
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Open Access (DOAJ)
DOI: 10.13998/j.cnki.issn1002-1248.23-0251
Chao Zuo, Jiaming Qian, Shijie Feng, Wei Yin et al. • Light Science & Applications
(2022)
With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, n...
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DOI: https://doi.org/10.1038/s41377-022-00714-x
PMC: 8866517
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona et al. • Drug Discovery Today
(2018)
Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other...
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DOI: https://doi.org/10.1016/j.drudis.2018.01.039
Zhong‐Qiu Zhao, Peng Zheng, Shou-Tao Xu, Xindong Wu • IEEE Transactions on Neural Networks and Learning Systems
(2019)
Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable...
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Open Access
DOI: https://doi.org/10.1109/tnnls.2018.2876865
Jiasi Chen, Xukan Ran • Proceedings of the IEEE
(2019)
Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things sensors, are generating data that need to be analyzed in...
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DOI: https://doi.org/10.1109/jproc.2019.2921977
Khawja Imran Masud, Md Yasin Zihad, Mehedi Hasan Shuvo, Mst Raonik Jannat et al. • PLoS ONE
(2026)
Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computa...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0356383
Srigiri Krishnapriya, Yepuganti Karuna • Frontiers in Oncology
(2024)
IntroductionBrain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process...
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Open Access (DOAJ)
DOI: 10.3389/fonc.2024.1347363
Lulu Jia, Qing Yang, Hanchen Jiang, Gang Huang et al. • BMC Medical Imaging
(2025)
Abstract Background The P53-mutated Hepatocellular Carcinoma (HCC) is an aggressive variant associated with vascular endothelial growth factor (VEGF) overexpression and increased microvascular density. This study aimed to develop an MRI-bas...
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Open Access (DOAJ)
DOI: 10.1186/s12880-025-02045-w
Yihan Chen, Xingyu Gu, Zhen Liu, Jia Liang • Remote Sensing
(2022)
Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful a...
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Open Access (DOAJ)
DOI: 10.3390/rs14081877
Dinggang G. Shen, Guorong Wu A, Heung‐Il Suk • Annual Review of Biomedical Engineering
(2017)
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical image...
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DOI: https://doi.org/10.1146/annurev-bioeng-071516-044442
PMC: 5479722
Justin M. Johnson, Taghi M. Khoshgoftaar • Journal Of Big Data
(2019)
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in...
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DOI: https://doi.org/10.1186/s40537-019-0192-5
Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay • ACM Computing Surveys
(2019)
With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applic...
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Open Access
DOI: https://doi.org/10.1145/3285029
Neil Thompson, Kristjan H. Greenewald, Keeheon Lee, Gabriel F. Manso (2023)
Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks.But this progress has come with ...
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DOI: https://doi.org/10.21428/bf6fb269.1f033948
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal et al. • Radiographics
(2017)
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...
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DOI: https://doi.org/10.1148/rg.2017170077
Ko-Tsung Hsu, Steven Guan, Parag V. Chitnis • Photoacoustics
(2023)
Iterative reconstruction has demonstrated superior performance in medical imaging under compressed, sparse, and limited-view sensing scenarios. However, iterative reconstruction algorithms are slow to converge and rely heavily on hand-craft...
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Open Access (DOAJ)
DOI: 10.1016/j.pacs.2023.100452
Lisha Nie, Siyi Li, Bing Wu, Yuhui Xiong et al. • iRADIOLOGY
(2024)
Abstract The development of 7‐Tesla (7T) magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths, especially in neuroscience research. This review investigates wh...
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Open Access (DOAJ)
DOI: 10.1002/ird3.92
Shiyao Duan, Jiaojiao Li, Rui Song, Yunsong Li et al. • Remote Sensing
(2023)
Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design. Additionally, models excl...
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Open Access (DOAJ)
DOI: 10.3390/rs15102619