Low-Rank Deep Convolutional Neural Network for Multitask Learning

Su, Fang; Shang, Hai-Yang; Wang, Jingyan

doi:10.1155/2019/7410701

Cited by 9 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, deep learning is a very important aspect in the field of machine learning, especially in the medical and other industries [ 17 , 18 ]. Machine learning relies on the process of computer recognition data, which is limited to a certain extent, and requires manpower and material resources to develop specific evaluation indicators for each mental disease [ 19 , 20 ]. These indicators are special and constantly change with the types of diseases, which is not conducive to the unified processing of intelligent data [ 21 – 23 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

Huang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Incidence rate of mental illness is increasing year by year with the development of city. The amount of modern medical data is huge and complex. In many cases, it is difficult to realize the rational allocation of resources, which puts forward an urgent demand for the artificial intelligence of modern medicine and brings great pressure to the development of the medical industry. The purpose of this study is to develop and construct a grey correlation analysis and related drug evaluation system of mental diseases based on deep convolution neural network. The establishment of the system can effectively improve the automation and intelligence of modern psychiatric treatment process. In this article, the grey correlation analysis of patient data is carried out, and then, the optimized deep convolution neural network is constructed. Combined with the medical knowledge base, the analysis of disease results is realized, and on this basis, the efficacy of related drugs in the treatment of mental diseases is evaluated. The results show that the advantage of the deep convolution neural network system is to effectively improve the induction rate. What’s more, compared with other algorithms, this algorithm has higher accuracy and efficiency. It improves the comprehensiveness and informatization of disease screening methods, improves the accuracy of screening, reduces the consumption of doctors’ human resources, and provides a theoretical basis for the digitization of the medical industry in the future.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

Huang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…As artificial intelligence and big data march forward continuously, they have gradually found applications in the medical field. How to extract effective features from massive [16,17]. In the study, the cascaded 3D deep residual network algorithm was applied to segment multimodal MRI images of stroke patients, which demonstrated great capacities in foci segmentation of stroke patients.…”

Section: Discussionmentioning

confidence: 99%

Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images

Pan

Zhang

Yang

et al. 2021

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

This study aimed to explore the application value of multimodal magnetic resonance imaging (MRI) images based on the deep convolutional neural network (Conv.Net) in the diagnosis of strokes. Specifically, four automatic segmentation algorithms were proposed to segment multimodal MRI images of stroke patients. The segmentation effects were evaluated factoring into DICE, accuracy, sensitivity, and segmentation distance coefficient. It was found that although two-dimensional (2D) full convolutional neural network-based segmentation algorithm can locate and segment the lesion, its accuracy was low; the three-dimensional one exhibited higher accuracy, with various objective indicators improved, and the segmentation accuracy of the training set and the test set was 0.93 and 0.79, respectively, meeting the needs of automatic diagnosis. The asymmetric 3D residual U-Net network had good convergence and high segmentation accuracy, and the 3D deep residual network proposed on its basis had good segmentation coefficients, which can not only ensure segmentation accuracy but also avoid network degradation problems. In conclusion, the Conv.Net model can accurately segment the foci of patients with ischemic stroke and is suggested in clinic.

show abstract

“…Code description and source code graphs can be transformed into directed and labeled multi-graphs. We use Relational Graph Convolutional Network (RGCN) 58,59 . RGCN is a kind of GNN to learn the node embedding from directed and labeled multi-graphs.…”

Section: /19mentioning

confidence: 99%

Efficient Deep Learning based Code Retrieval using Unified Graph Structure and Semantic Graph Matching Encoder

Bashir

Rahman

2023

Preprint

View full text Add to dashboard Cite

Computer programmers frequently use code retrieval systems to reuse existing code snippets from open-source repositories. These systems search a large collection of code snippets to retrieve the most relevant ones based on a text query. However, capturing semantic information between user queries and code snippets presents a key challenge in this task. Previous works have primarily used natural language processing (NLP) approaches to calculate relevance, but these approaches cannot capture deep structural and semantic dependencies between queries and snippets. In this study, we proposed a novel approach called Deep Graph-Based Semantic Representation and Matching (DeepGSR) for code retrieval. Our approach transforms queries and code snippets into a unified graph-structure representation and then retrieves relevant code snippets using a novel graph matching and retrieval approach. DeepGSR learns fine-grained relevance between queries and code snippets using cross-attention-based semantic matching operators, in addition to extracting structural information. We evaluated our approach using two publicly available code retrieval datasets, and the experiments demonstrate that DeepGSR efficiently retrieves relevant code snippets for a given query and significantly outperforms existing code retrieval approaches.

show abstract

Low-Rank Deep Convolutional Neural Network for Multitask Learning

Cited by 9 publications

References 43 publications

[Retracted] Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

[Retracted] Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images

Efficient Deep Learning based Code Retrieval using Unified Graph Structure and Semantic Graph Matching Encoder

Contact Info

Product

Resources

About