Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

Information Fusion, Год журнала: 2020, Номер 67, С. 208 - 229

Опубликована: Окт. 9, 2020

Язык: Английский

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Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network

Information Processing & Management, Год журнала: 2020, Номер 58(2), С. 102439 - 102439

Опубликована: Дек. 2, 2020

Язык: Английский

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A comprehensive survey on multimodal medical signals fusion for smart healthcare systems

Information Fusion, Год журнала: 2021, Номер 76, С. 355 - 375

Опубликована: Июль 5, 2021

Язык: Английский

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Facial expression recognition via ResNet-50

International Journal of Cognitive Computing in Engineering, Год журнала: 2021, Номер 2, С. 57 - 64

Опубликована: Фев. 23, 2021

As one of the most important directions in field computer vision, facial emotion recognition plays an role people's daily work and life. Human based on expressions is great significance application intelligent human-computer interaction. However, current research recognition, there are some problems such as poor generalization ability network model low robustness system. In this content, we propose a method feature extraction using deep residual ResNet-50, which combines convolutional neural for recognition. Through experimental simulation specified data set, it can be proved that superior to mainstream models performance detection.

Язык: Английский

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An overview of deep learning methods for multimodal medical data mining

Expert Systems with Applications, Год журнала: 2022, Номер 200, С. 117006 - 117006

Опубликована: Апрель 4, 2022

Язык: Английский

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A Heterogeneously Integrated Spiking Neuron Array for Multimode‐Fused Perception and Object Classification

Advanced Materials, Год журнала: 2022, Номер 34(24)

Опубликована: Апрель 16, 2022

Multimode-fused sensing in the somatosensory system helps people obtain comprehensive object properties and make accurate judgments. However, building such multisensory systems with conventional metal-oxide-semiconductor technology presents serious device integration circuit complexity challenges. Here, a multimode-fused spiking neuron (MFSN) compact structure to achieve human-like perception is reported. The MFSN heterogeneously integrates pressure sensor process NbOx -based memristor sense temperature. Using this MFSN, analog information can be fused into one spike train, showing excellent data compression conversion capabilities. Moreover, both temperature are distinguished from spikes by decoupling output frequencies amplitudes, supporting multimodal tactile perception. Then, 3 × array fabricated, frequency patterns fed neural network for enhanced pattern recognition. Finally, larger simulated classifying objects different shapes, temperatures, weights, validating feasibility of MFSNs practical applications. proof-of-concept enable sensory contribute development highly intelligent robotics.

Язык: Английский

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Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

Genomics Proteomics & Bioinformatics, Год журнала: 2022, Номер 20(5), С. 850 - 866

Опубликована: Окт. 1, 2022

The recent development of imaging and sequencing technologies enables systematic advances in the clinical study lung cancer. Meanwhile, human mind is limited effectively handling fully utilizing accumulation such enormous amounts data. Machine learning-based approaches play a critical role integrating analyzing these large complex datasets, which have extensively characterized cancer through use different perspectives from accrued In this article, we provide an overview machine that strengthen varying aspects diagnosis therapy, including early detection, auxiliary diagnosis, prognosis prediction, immunotherapy practice. Moreover, highlight challenges opportunities for future applications learning

Язык: Английский

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Artificial intelligence-based methods for fusion of electronic health records and imaging data

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Окт. 26, 2022

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal sources contributes to a better understanding of human provides optimal personalized healthcare. The most important question when using is how fuse them-a field growing interest among researchers. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion different modalities provide insights. To this end, scoping review, we focus on synthesizing analyzing literature that uses AI techniques for clinical applications. More specifically, studies only fused EHR with imaging develop various methods We present comprehensive analysis strategies, diseases outcomes which was used, ML algorithms used perform each application, available datasets. followed PRISMA-ScR (Preferred Reporting Items Systematic Reviews Meta-Analyses Extension Scoping Reviews) guidelines. searched Embase, PubMed, Scopus, Google Scholar retrieve relevant studies. After pre-processing screening, extracted from 34 fulfilled inclusion criteria. found fusing increasing doubling 2020 2021. In our analysis, typical workflow observed: feeding raw data, by applying conventional (ML) or deep (DL) algorithms, finally, evaluating through outcome predictions. Specifically, early technique applications (22 out studies). multimodality models outperformed traditional single-modality same task. Disease diagnosis prediction were common (reported 20 10 studies, respectively) perspective. Neurological disorders dominant category (16 From an perspective, (19 studies), DL Multimodal included mostly private repositories (21 Through offer new insights researchers interested knowing current state knowledge within research field.

Язык: Английский

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Modern views of machine learning for precision psychiatry

Patterns, Год журнала: 2022, Номер 3(11), С. 100602 - 100602

Опубликована: Ноя. 1, 2022

In light of the National Institute Mental Health (NIMH)'s Research Domain Criteria (RDoC), advent functional neuroimaging, novel technologies and methods provide new opportunities to develop precise personalized prognosis diagnosis mental disorders. Machine learning (ML) artificial intelligence (AI) are playing an increasingly critical role in era precision psychiatry. Combining ML/AI with neuromodulation can potentially explainable solutions clinical practice effective therapeutic treatment. Advanced wearable mobile also call for digital phenotyping health. this review, we a comprehensive review ML methodologies applications by combining neuromodulation, advanced psychiatry practice. We further molecular cross-species biomarker identification discuss AI (XAI) closed human-in-the-loop manner highlight potential multi-media information extraction multi-modal data fusion. Finally, conceptual practical challenges future research.

Язык: Английский

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Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care

Frontiers in Cardiovascular Medicine, Год журнала: 2022, Номер 9

Опубликована: Апрель 27, 2022

Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized timely. Sources data for tools include multiple modalities such as electronic medical records (EMR), radiology images, genetic repositories, name a few. While historically, these were utilized in silos, new machine learning (ML) deep (DL) technologies enable integration sources produce multi-modal insights. Data fusion, which integrates from using ML DL techniques, has been growing interest its application medicine. In this paper, we review state-of-the-art research that focuses on how latest techniques fusion are providing scientific clinical insights specific field cardiovascular With capabilities, clinicians researchers alike will advance diagnosis treatment diseases (CVD) deliver timely, accurate, precise patient care.

Язык: Английский

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