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2.
Sensors (Basel) ; 22(11)2022 May 30.
Artigo em Inglês | MEDLINE | ID: mdl-35684759

RESUMO

Vehicle-infrastructure cooperative perception is an ingenious way to eliminate environmental perception blind areas of connected and autonomous vehicles (CAVs). However, if the infrastructure transmits all environmental information to the nearby CAVs, the transmission load is so heavy that it causes a waste of network resources, such as time and bandwidth, because parts of the information are redundant for the CAVs. It is an efficient manner for the infrastructure to merely transmit the information about objects which cannot be perceived by the CAVs. Therefore, the infrastructure needs to predict whether an object is perceptible for a CAV. In this paper, a machine-leaning-based model is established to settle this problem, and a data filter is also designed to enhance the prediction accuracy in various scenarios. Based on the proposed model, the infrastructure transmits the environmental information selectively, which significantly reduces the transmission load. The experiments prove that the prediction accuracy of the model achieves up to 95%, and the transmission load is reduced by 55%.


Assuntos
Veículos Automotores , Percepção , Coleta de Dados
3.
Sensors (Basel) ; 22(11)2022 Jun 02.
Artigo em Inglês | MEDLINE | ID: mdl-35684864

RESUMO

The deployment of any UAV application in precision agriculture involves the development of several tasks, such as path planning and route optimization, images acquisition, handling emergencies, and mission validation, to cite a few. UAVs applications are also subject to common constraints, such as weather conditions, zonal restrictions, and so forth. The development of such applications requires the advanced software integration of different utilities, and this situation may frighten and dissuade undertaking projects in the field of precision agriculture. This paper proposes the development of a Web and MATLAB-based application that integrates several services in the same environment. The first group of services deals with UAV mission creation and management. It provides several pieces of flight conditions information, such as weather conditions, the KP index, air navigation maps, or aeronautical information services including notices to Airmen (NOTAM). The second group deals with route planning and converts selected field areas on the map to an UAV optimized route, handling sub-routes for long journeys. The third group deals with multispectral image processing and vegetation indexes calculation and visualizations. From a software development point of view, the app integrates several monolithic and independent programs around the MATLAB Runtime package with an automated and transparent data flow. Its main feature consists in designing a plethora of executable MATLAB programs, especially for the route planning and optimization of UAVs, images processing and vegetation indexes calculations, and running them remotely.


Assuntos
Agricultura , Tecnologia de Sensoriamento Remoto , Agricultura/métodos , Coleta de Dados , Processamento de Imagem Assistida por Computador , Tecnologia de Sensoriamento Remoto/métodos
4.
Contrast Media Mol Imaging ; 2022: 2058284, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35685674

RESUMO

In this paper, the medical Internet of things (IoT) is used to pool data from clinical trials of pulmonary nodules, and on this basis, intelligent differential diagnosis techniques are investigated. A filtered orthogonal frequency division multiplexing model based on polarisation coding is proposed, where the input data are fed to a modulator after polarisation cascade coding, and the system performance is analysed under a medical Internet of things modulated additive Gaussian white noise channel. The above polarisation-coded filtered orthogonal frequency division multiplexing system components are applied to electroencephalogram (EEG) signal transmission, to which a threshold compression module and a vector reconstruction module are added to address the system power burden associated with the acquisition and transmission of large amounts of real-time EEG data in the medical IoT. In the threshold compression module, the inherent characteristics of EEG signals are analysed, and the generated EEG data are decomposed into multiple symbolic streams and compressed by applying different thresholds to improve the compression ratio while ensuring the quality of service of the application. A deep neural network-based approach is proposed for the detection and diagnosis of lung nodules. Automatic identification and measurement of simulated lung nodules and the corresponding volumes of nodules in images under different conditions are applied. The sensitivity of each AIADS in identifying lung nodules under different convolution kernel conditions, false positives (FP), false negatives (FN), relative volume errors (RVE), the miss detection rate (MDR) for different types of lung nodules, and the performance of each system in predicting the four types of nodules are calculated. In this paper, an interpretable multibranch feature convolutional neural network model is proposed for the diagnosis of benign and malignant lung nodules. It is demonstrated that the proposed model not only yields interpretable lung nodule classification results but also achieves better lung nodule classification performance with an accuracy rate of 97.8%.


Assuntos
Internet das Coisas , Neoplasias Pulmonares , Nódulo Pulmonar Solitário , Coleta de Dados , Diagnóstico Diferencial , Humanos , Neoplasias Pulmonares/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Nódulo Pulmonar Solitário/diagnóstico por imagem , Nódulo Pulmonar Solitário/patologia , Tomografia Computadorizada por Raios X/métodos
5.
Comput Intell Neurosci ; 2022: 3772108, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35694602

RESUMO

The village with historical and cultural accumulation not only is the witness of the interaction between human activities and natural environment but also contains a lot of intangible cultural heritage. It is an important research object in the history of human social development and has great reference value for the construction of modern Humayun settlements. With the continuous change of modern rural landscape, the traditional engineering drawing cannot meet the needs of people for spatial information measurement. There are some problems such as slow drawing, low precision, and poor benefit, so computer drawing must be used. Therefore, this paper puts forward the research on rural landscape spatial information recording and protection based on 3D point cloud technology under the background of Internet of Things and realizes the recording of rural landscape spatial information through the construction of 3D point cloud spatial information recording model. The experimental results show that the improved unit point cloud spatial information recording model has better feature extraction effect and feature point extraction efficiency, and can classify the point cloud more carefully. At the same time, the scene 3D point cloud classification method based on conditional random field can better deal with the interference factors in the outdoor landscape and show a better classification effect.


Assuntos
Computação em Nuvem , Internet das Coisas , Coleta de Dados , Humanos , Internet
6.
Malar J ; 21(1): 185, 2022 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-35690756

RESUMO

BACKGROUND: Malaria is a major cause of morbidity and mortality globally, especially in sub-Saharan Africa. Widespread resistance to pyrethroids threatens the gains achieved by vector control. To counter resistance to pyrethroids, third-generation indoor residual spraying (3GIRS) products have been developed. This study details the results of a multi-country cost and cost-effectiveness analysis of indoor residual spraying (IRS) programmes using Actellic®300CS, a 3GIRS product with pirimiphos-methyl, in sub-Saharan Africa in 2017 added to standard malaria control interventions including insecticide-treated bed nets versus standard malaria control interventions alone. METHODS: An economic evaluation of 3GIRS using Actellic®300CS in a broad range of sub-Saharan African settings was conducted using a variety of primary data collection and evidence synthesis methods. Four IRS programmes in Ghana, Mali, Uganda, and Zambia were included in the effectiveness analysis. Cost data come from six IRS programmes: one in each of the four countries where effect was measured plus Mozambique and a separate programme conducted by AngloGold Ashanti Malaria Control in Ghana. Financial and economic costs were quantified and valued. The main indicator for the cost was cost per person targeted. Country-specific case incidence rate ratios (IRRs), estimated by comparing IRS study districts to adjacent non-IRS study districts or facilities, were used to calculate cases averted in each study area. A deterministic analysis and sensitivity analysis were conducted in each of the four countries for which effectiveness evaluations were available. Probabilistic sensitivity analysis was used to generate plausibility bounds around the incremental cost-effectiveness ratio estimates for adding IRS to other standard interventions in each study setting as well as jointly utilizing data on effect and cost across all settings. RESULTS: Overall, IRRs from each country indicated that adding IRS with Actellic®300CS to the local standard intervention package was protective compared to the standard intervention package alone (IRR 0.67, [95% CI 0.50-0.91]). Results indicate that Actellic®300CS is expected to be a cost-effective (> 60% probability of being cost-effective in all settings) or highly cost-effective intervention across a range of transmission settings in sub-Saharan Africa. DISCUSSION: Variations in the incremental costs and cost-effectiveness likely result from several sources including: variation in the sprayed wall surfaces and house size relative to household population, the underlying malaria burden in the communities sprayed, the effectiveness of 3GIRS in different settings, and insecticide price. Programmes should be aware that current recommendations to rotate can mean variation and uncertainty in budgets; programmes should consider this in their insecticide-resistance management strategies. CONCLUSIONS: The optimal combination of 3GIRS delivery with other malaria control interventions will be highly context specific. 3GIRS using Actellic®300CS is expected to deliver acceptable value for money in a broad range of sub-Saharan African malaria transmission settings.


Assuntos
Inseticidas , Malária , Compostos Organotiofosforados , Piretrinas , Análise Custo-Benefício , Coleta de Dados , Humanos , Malária/epidemiologia , Mali , Controle de Mosquitos/métodos
7.
Spat Spatiotemporal Epidemiol ; 41: 100506, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35691640

RESUMO

This study tested spatio-temporal model prediction accuracy and concurrent validity of observed neighborhood physical disorder collected from virtual audits of Google Street View streetscapes. We predicted physical disorder from spatio-temporal regression Kriging models based on measures at three dates per each of 256 streestscapes (n = 768 data points) across an urban area. We assessed model internal validity through cross validation and external validity through Pearson correlations with respondent-reported perceptions of physical disorder from a breast cancer survivor cohort. We compared validity among full models (both large- and small-scale spatio-temporal trends) versus large-scale only. Full models yielded lower prediction error compared to large-scale only models. Physical disorder predictions were lagged at uniform distances and dates away from the respondent-reported perceptions of physical disorder. Correlations between perceived and observed physical disorder predicted from the full model were higher compared to that of the large-scale only model, but only at locations and times closest to the respondent's exact residential address and questionnaire date. A spatio-temporal Kriging model of observed physical disorder is valid.


Assuntos
Projetos de Pesquisa , Características de Residência , Coleta de Dados , Humanos , Análise Espacial , Caminhada
8.
Front Public Health ; 10: 893770, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35664094

RESUMO

Background: The demand and consumption of immunoglobulins (IgGs) are growing, and there are many difficulties in obtaining supplies. The aim of the study was to analyze the evolution of IgG consumption and cost over a decade, describe the measures implemented for clinical management in the context of regional public health system, and evaluate the initial impact of these measures. Methods: We performed a retrospective longitudinal study including patients of all public health systems in Catalonia. First, we analyzed data on consumption and cost of IgGs during a period between 1 January, 2010 and 31 December 2021. Second, we analyzed the impact of a set of regional measures in terms of annual consumption and cost of IgGs. Regional measures were based on rational evidence-based measures and computer registries. We compared the data of year before applying intervention measures (1 January and 31 December 2020) with data of year after applying clinical management interventions (1 January and 31 December 2021). In addition, detailed information on clinical indications of IgG use between 1 January and 31 December 2021 was collected. Results: Overall, in terms of population, the consumption of IgGs (g/1,000 inhabitants) increased from 40.4 in 2010 to 94.6 in 2021. The mean cost per patient increased from €10,930 in 2010 to €15,595 in 2021. After implementing the measures, the mean annual estimated consumption per patient in 2021 was statistically lower than the mean annual estimated consumption per patient in 2020 (mean difference -47 g, 95% CI -62.28 g, -31.72 g, p = 0.03). The mean annual estimated cost per patient in 2021 was also lower than the mean annual estimated cost per patient in 2020 (the mean difference was -€1,492, 95% CI -€2,132.12, -€851.88; p = 0.027). In 2021, according to evidence-based classification, 75.66% treatments were prescribed for a demonstrated therapeutic evidence-based indication, 12.17% for a developed therapeutic evidence-based indication, 4.66% for non-evidence-based therapeutic role indication, and 8.1% could not be classified because of lack of information. Conclusion: The annual consumption and cost of IgGs have grown steadily over the last decade in our regional public health system. After implementing a set of regional measures, the annual consumption of IgGs per patient and annual cost per patient decreased. However, the decrease has occurred in the context of the coronavirus disease 2019 (COVID-19) pandemic, which may have influenced their clinical use. Managing the use of IgGs through a rational plan with strategies including evidence-based and data collection may be useful in a shortage situation with growing demand. Registries play a key role in collection of systematic data to analyze, synthesize, and obtain valuable information for decision support. The action developed needs close monitoring in order to verify its effectiveness.


Assuntos
COVID-19 , Coleta de Dados , Humanos , Imunoglobulina G , Estudos Longitudinais , Racionalização , Estudos Retrospectivos
9.
Comput Intell Neurosci ; 2022: 4391491, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35665281

RESUMO

Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category. Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases. In our approach, an improved CSP-stage backbone network is first formed to offer massive channel-shuffled features in multiple granularities. Secondly, relying on the multilevel attention mechanism, the feature aggregation enhancement module is designed to exploit distinguishable fine-grained features representing different discriminating parts. Meanwhile, the graphic convolution module is constructed to analyse the graph-correlated representation of part-specific interrelationships by regularizing semantic features into the high-order tensor space. With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. Extensive experiments on several public fine-grained disease and pest datasets demonstrate that the proposed GHA-Net achieves better performances in accuracy and efficiency surpassing several other existing models and is more suitable for fine-grained identification applications in complex scenes.


Assuntos
Redes Neurais de Computação , Semântica , Coleta de Dados
10.
BMC Bioinformatics ; 23(1): 214, 2022 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-35668357

RESUMO

BACKGROUND: Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring. RESULTS: We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations. CONCLUSION: Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience.


Assuntos
Produtos Agrícolas , Melhoramento Vegetal , Coleta de Dados , Fenótipo
11.
Comput Intell Neurosci ; 2022: 3418269, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35669666

RESUMO

To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.


Assuntos
Algoritmos , Software , Coleta de Dados
12.
PLoS One ; 17(6): e0268134, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35675350

RESUMO

One of the core tenets of a well-functioning representative democracy is that the people who vote to elect government officials are representative of the public. Here we reinforce the idea that reality is far from this lofty ideal. We document the extent and nature of inequities in voter participation in the United States with a level of granularity and precision that previous research has not afforded. To do so, we use a unique nationwide dataset of approximately 400 million validated voting records across multiple election cycles. With this novel dataset, we document large and persistent gaps in voter turnout by race, age, and political affiliation. Minority citizens, young people, and those who support the Democratic Party are much less likely to vote than whites, older citizens, and Republican Party supporters. Minorities, youth, and democrats are also much more likely to live in local communities where fewer individuals vote-areas that we term turnout deserts. Turnout deserts are especially pernicious given that they are self-reinforcing-bolstered by the social dynamics that fundamentally shape citizens' voting patterns. Our results show just how glaring inequities in political participation are in the US. These patterns threaten the very fabric of our democracy and fundamentally shift the balance of political power in the halls of government towards the interests of whites, older citizens, and republicans. They illustrate that participation in the United States is strikingly unequal-far from the ideals that this country has long aspired to.


Assuntos
Política , Adolescente , Coleta de Dados , Humanos , Estados Unidos
13.
Stud Health Technol Inform ; 290: 158-162, 2022 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-35672991

RESUMO

Electronic patient charts are essential for follow-up and multi-disciplinary care, but either take up an exorbitant amount of time during the patient encounter using a key-stroke entry system, or suffer from poor recall when made long after the encounter. Transcribing in-situ, natural dictations by the clinician, recorded during the encounter, with minimal workflow impact, is a promising solution. However, human transcription requires significant manual resources, whereas automated transcription currently lacks the accuracy for specialized clinical language. Our ultimate goal is to automate clinical transcription, particularly for Emergency Departments, with as an end-result a structured SOAP report. Towards this goal, we present the Adaptive Clinical Transcription System (ACTS). We compare the accuracy and processing times of state-of-the-art speech recognition tools, studying the feasibility of streaming-style dynamic transcription and opportunities of incremental learning.


Assuntos
Processamento de Linguagem Natural , Fala , Coleta de Dados , Humanos , Idioma , Fluxo de Trabalho
14.
Stud Health Technol Inform ; 290: 309-313, 2022 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-35673024

RESUMO

The rapid growth of clinical trials launched in recent years poses significant challenges for accurate and efficient trial search. Keyword-based clinical trial search engines require users to construct effective queries, which can be a difficult task given complex information needs. In this study, we present an interactive clinical trial search interface that retrieves trials similar to a target clinical trial. It enables user configuration of 13 clinical trial features and 4 metrics (Jaccard similarity, semantic-based similarity, temporal overlap and geographical distance) to measure pairwise trial similarities. Among 1,007 coronavirus disease 2019 (COVID-19) trials conducted in the United States, 91.9% were found to have similar trials with the similarity threshold being 0.85 and 43.8% were highly similar with the threshold 0.95. A simulation study using 3 groups of similar trials curated by COVID-19 clinical trial reviews demonstrates the precision and recall of the search interface.


Assuntos
COVID-19 , Benchmarking , Coleta de Dados , Humanos , Ferramenta de Busca , Semântica
15.
Stud Health Technol Inform ; 290: 442-446, 2022 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-35673053

RESUMO

Information technologies have the potential to increase the safety of healthcare and advance safety science. However, it is now well known that health information systems may also inadvertently introduce new forms of error known as technology-induced error. Such errors may be difficult to detect as they may only appear under conditions of system use in real healthcare settings. In this paper, the authors explore the use and assessment of recall and safety alerts for both identifying and learning from technology-induced error. Publically available safety and recall reports from Canada were analyzed to identify opportunities to improve organizational learning from technology-induced errors. Although a range of error types were identified, it was found that none of the reports provided detailed information about the underlying technical circumstances that led to the need for a recall. Implications for future reporting systems to support learning from technology-induced error are discussed.


Assuntos
Sistemas de Informação em Saúde , Coleta de Dados , Atenção à Saúde , Registros , Tecnologia
16.
Anesthesiol Clin ; 40(2): 315-323, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35659403

RESUMO

Early-career physicians face a broad range of challenges unique to their phase of life and career. Beginning in residency, anesthesiologists encounter stressors unique to their work environment, which, when coupled with their personal life demands, places significant burden and creates potential for burnout. In this article, the authors review the literature to explore the contributors of burnout in early-career anesthesiologists, evaluate the relationship between compassionate care and empathic distress, and propose strategies to prevent and treat burnout in this specific subset of anesthesiologists.


Assuntos
Esgotamento Profissional , Internato e Residência , Médicos , Esgotamento Profissional/prevenção & controle , Coleta de Dados , Humanos , Satisfação no Emprego
17.
Arthroscopy ; 38(6): 1954-1955, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35660187

RESUMO

Knee osteotomy is a valuable treatment option for younger knee osteoarthritis patients. Improved surgical techniques, including double-level osteotomies to address femoral and tibial malalignment, have led to reappreciation of this joint-sparing alternative to knee arthroplasty. Yet, postoperative ability to resume sport and work at the desired level needs further improvement. We believe that timely surgery, optimized perioperative care, including evidence-based advice for resumption of activities, and prospective data collection are interesting next steps in this process.


Assuntos
Osteoartrite do Joelho , Volta ao Esporte , Coleta de Dados , Humanos , Articulação do Joelho/cirurgia , Osteoartrite do Joelho/cirurgia , Osteotomia/métodos , Assistência Perioperatória , Tíbia/cirurgia
18.
Nat Commun ; 13(1): 3094, 2022 Jun 02.
Artigo em Inglês | MEDLINE | ID: mdl-35655064

RESUMO

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI".


Assuntos
Inteligência Artificial , Inteligência , Coleta de Dados , Humanos
19.
BMC Cancer ; 22(1): 604, 2022 Jun 02.
Artigo em Inglês | MEDLINE | ID: mdl-35655179

RESUMO

BACKGROUND: Real-world data (RWD) is increasingly being embraced as an invaluable source of information to address clinical and policy-relevant questions that are unlikely to ever be answered by clinical trials. However, the largely unrealised potential of RWD is the value to be gained by supporting prospective studies and translational research. Here we describe the design and implementation of an Australian brain cancer registry, BRAIN, which is pursuing these opportunities. METHODS: BRAIN was designed by a panel of clinicians in conjunction with BIOGRID to capture comprehensive clinical data on patients diagnosed with brain tumours from diagnosis through treatment to recurrence or death. Extensive internal and external testing was undertaken, followed by implementation at multiple sites across Victoria and Tasmania. RESULTS: Between February 2021 and December 2021, a total of 350 new patients from 10 sites, including one private and two regional, were entered into BRAIN. Additionally, BRAIN supports the world's first registry trial in neuro-oncology, EX-TEM, addressing the optimal duration of post-radiation temozolomide; and BioBRAIN, a dedicated brain tumour translational program providing a pipeline for biospecimen collection matched with linked clinical data. CONCLUSIONS: Here we report on the first data collection effort in brain tumours for Australia, which we believe to be unique worldwide given the number of sites and patients involved and the extent to which the registry resource is being leveraged to support clinical and translational research. Further directions such as passive data flow and data linkages, use of artificial intelligence and inclusion of patient-entered data are being explored.


Assuntos
Inteligência Artificial , Neoplasias Encefálicas , Neoplasias Encefálicas/epidemiologia , Neoplasias Encefálicas/terapia , Coleta de Dados , Humanos , Estudos Prospectivos , Sistema de Registros , Vitória
20.
Comput Intell Neurosci ; 2022: 9693767, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35655505

RESUMO

Traffic target tracking is a core task in intelligent transportation system because it is useful for scene understanding and vehicle autonomous driving. Most state-of-the-art (SOTA) multiple object tracking (MOT) methods adopt a two-step procedure: object detection followed by data association. The object detection has made great progress with the development of deep learning. However, the data association still heavily depends on hand crafted constraints, such as appearance, shape, and motion, which need to be elaborately trained for a special object. In this study, a spatial-temporal encoder-decoder affinity network is proposed for multiple traffic targets tracking, aiming to utilize the power of deep learning to learn a robust spatial-temporal affinity feature of the detections and tracklets for data association. The proposed spatial-temporal affinity network contains a two-stage transformer encoder module to encode the features of the detections and the tracked targets at the image level and the tracklet level, aiming to capture the spatial correlation and temporal history information. Then, a spatial transformer decoder module is designed to compute the association affinity, where the results from the two-stage transformer encoder module are fed back to fully capture and encode the spatial and temporal information from the detections and the tracklets of the tracked targets. Thus, efficient affinity computation can be applied to perform data association in online tracking. To validate the effectiveness of the proposed method, three popular multiple traffic target tracking datasets, KITTI, UA-DETRAC, and VisDrone, are used for evaluation. On the KITTI dataset, the proposed method is compared with 15 SOTA methods and achieves 86.9% multiple object tracking accuracy (MOTA) and 85.71% multiple object tracking precision (MOTP). On the UA-DETRAC dataset, 12 SOTA methods are used to compare with the proposed method, and the proposed method achieves 20.82% MOTA and 35.65% MOTP, respectively. On the VisDrone dataset, the proposed method is compared with 10 SOTA trackers and achieves 40.5% MOTA and 74.1% MOTP, respectively. All those experimental results show that the proposed method is competitive to the state-of-the-art methods by obtaining superior tracking performance.


Assuntos
Movimento (Física) , Coleta de Dados
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