Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network [PDF]
Frontiers in Genetics, 2019 Measuring conditional relatedness, the degree of relation between a pair of genes in a certain condition, is a basic but difficult task in bioinformatics, as traditional co-expression analysis methods rely on co-expression similarities, well known with ...
Yan Wang +6 more
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Fully Convolutional Neural Network for Vehicle Speed and Emergency-Brake Prediction [PDF]
Sensors, 2023 Ego-vehicle state prediction represents a complex and challenging problem for self-driving and autonomous vehicles. Sensorial information and on-board cameras are used in perception-based solutions in order to understand the state of the vehicle and the ...
Razvan Itu, Radu Danescu
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Pelvic bone tumor segmentation fusion algorithm based on fully convolutional neural network and conditional random field [PDF]
Journal of Bone OncologyBackground and objective: Pelvic bone tumors represent a harmful orthopedic condition, encompassing both benign and malignant forms. Addressing the issue of limited accuracy in current machine learning algorithms for bone tumor image segmentation, we ...
Shiqiang Wu +6 more
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Fully Convolutional Neural Network Structure and Its Loss Function for Image Classification
IEEE Access, 2022 The overall structure of a convolutional neural network classifier includes multiple convolutional layers and one or more linear layers. Due to the fully connected characteristics of linear layer networks, there are usually many parameters, which may ...
Qiuyu Zhu, Xuewen Zu
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Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network [PDF]
Frontiers in Genetics, 2023 Introduction: CircRNA-protein binding plays a critical role in complex biological activity and disease. Various deep learning-based algorithms have been proposed to identify CircRNA-protein binding sites.
Zhen Shen +7 more
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A fully convolutional neural network for the quantification of mitral regurgitation in echocardiography. [PDF]
Quant Imaging Med SurgMitral regurgitation (MR) is the most common form of valvular heart disease (VHD), and the accurate assessment of MR severity is critical for clinical management. However, the quantitative assessment of MR is intricate and time-consuming, posing challenges for physicians in ensuring the precision of the results.
Zhong L +7 more
europepmc +3 more sources
Generating Multi‐Depth 3D Holograms Using a Fully Convolutional Neural Network [PDF]
Advanced ScienceEfficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi‐depth phase‐only holograms using a fully convolutional neural network (FCN).
Xingpeng Yan +9 more
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A Fully Convolutional Neural Network for Wood Defect Location and Identification [PDF]
IEEE Access, 2019 Defect detection on solid wood surface has two main problems: (1) the real-time performance of the available methods are poor despite good detection accuracy, and (2) the defect extraction process is complicated.
Ting He +5 more
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Buildings, 2022 Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs).
Wenzhuo Zhang +6 more
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Fully Convolutional Neural Network with Attention Module for Semantic Segmentation [PDF]
Jisuanji kexue yu tansuo, 2022 A fully convolutional neural network is a powerful end-to-end model that is widely used in the field of semantic segmentation and has achieved great success.
OU Yangliu, HE Xi, QU Shaojun
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