Text Detection with Deep Neural Network System Based on Overlapped Labels and a Hierarchical Segmentation of Feature Maps

2019 ◽  
Vol 17 (6) ◽  
pp. 1599-1610 ◽  
Author(s):  
Hong-Hyun Kim ◽  
Jea-Ho Jo ◽  
Zhu Teng ◽  
Dong-Joong Kang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Text Detection ◽  
Network System ◽  
Feature Maps ◽  
Hierarchical Segmentation ◽  
Neural Network System

scholarly journals Antibody Watch: Text mining antibody specificity from the literature

2021 ◽  
Vol 17 (5) ◽  
pp. e1008967
Author(s):  
Chun-Nan Hsu ◽  
Chia-Hui Chang ◽  
Thamolwan Poopradubsil ◽  
Amanda Lo ◽  
Karen A. William ◽  
...  
Keyword(s):  
Neural Network ◽  
Text Mining ◽  
Knowledge Base ◽  
Experimental Evaluation ◽  
Deep Neural Network ◽  
Network System ◽  
Antibody Specificity ◽  
Joint Task ◽  
Neural Network System ◽  
Reliable Knowledge

Antibodies are widely used reagents to test for expression of proteins and other antigens. However, they might not always reliably produce results when they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable research results. While many proposals have been developed to deal with the problem of antibody specificity, it is still challenging to cover the millions of antibodies that are available to researchers. In this study, we investigate the feasibility of automatically generating alerts to users of problematic antibodies by extracting statements about antibody specificity reported in the literature. The extracted alerts can be used to construct an “Antibody Watch” knowledge base containing supporting statements of problematic antibodies. We developed a deep neural network system and tested its performance with a corpus of more than two thousand articles that reported uses of antibodies. We divided the problem into two tasks. Given an input article, the first task is to identify snippets about antibody specificity and classify if the snippets report that any antibody exhibits non-specificity, and thus is problematic. The second task is to link each of these snippets to one or more antibodies mentioned in the snippet. The experimental evaluation shows that our system can accurately perform the classification task with 0.925 weighted F1-score, linking with 0.962 accuracy, and 0.914 weighted F1 when combined to complete the joint task. We leveraged Research Resource Identifiers (RRID) to precisely identify antibodies linked to the extracted specificity snippets. The result shows that it is feasible to construct a reliable knowledge base about problematic antibodies by text mining.

scholarly journals Novel artificial intelligent/neural network system for staging of nonalcoholic steatohepatitis

2021 ◽  
Author(s):  
Takeshi Okanoue ◽  
Toshihide Shima ◽  
Yasuhide Mitsumoto ◽  
Atsushi Umemura ◽  
Kanji Yamaguchi ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonalcoholic Steatohepatitis ◽  
Network System ◽  
Artificial Intelligent ◽  
Neural Network System

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

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