RDC-SAL: Refine distance compensating with quantum scale-aware learning for crowd counting and localization

Hu, Ruihan; Tang, Zhi-Ri; Wu, Edmond Q.; Mo, Qinglong; Yang, Rui; Li, Jingbin

doi:10.1007/s10489-022-03238-4

Cited by 13 publications

(10 citation statements)

References 55 publications

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“…China's intangible cultural heritage demonstrates that the splendid achievements of Chinese civilization are not only endless treasures containing rich knowledge, wisdom, and art, but also an important source of firm cultural confidence. At present, the existing literature from China Journal Net, Wanfang Database, and Conference Papers Index are mainly focused on the value of intangible cultural heritage [8], protection and utilization [9], revitalization and inheritance [10], tourism development [11], and education and teaching research [12]. In recent years, we have made great efforts to protect and utilize intangible cultural heritage in China, but there are also some problems, such as the destruction of the living space that unites the public's emotional memory, the distortion of authenticity, the lack of successors, and the marginalization.…”

Section: Introductionmentioning

confidence: 99%

Spatial Differentiation of Intangible Cultural Heritage in South China and Its Influencing Factors

Cai

2022

Wireless Communications and Mobile Computing

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In recent years, a lot of work has been done in the protection and utilization of intangible cultural heritage in South China, while there are dilemmas such as the variation of inheritance content, the extinction of heritage, and the absence of inheritors, it is urgent to strengthen the protection of intangible cultural heritage with realistic reasons. This paper adopts human geography research methods, based on SPSS mathematical statistics, GIS spatial analysis, and econometric models, to analyze the spatial differentiation characteristics of 10 types of intangible cultural heritage in the region and their influencing factors based on 1,708 intangible cultural heritage data from various regions in South China, according to five regions, Guangdong, Hainan, Guangxi, Hong Kong, and Macao. It was found that (1) the intangible cultural heritage in South China is distributed in clusters, with the Pearl River Delta in Guangdong and Guinan in Guangxi being the core areas of high-density intangible cultural heritage; (2) the characteristics of spatial differentiation of intangible cultural heritage in the region are influenced by a combination of factors, among which the pattern distribution is obviously influenced by cultural factors such as the distribution of ethnic minorities and the number of museums. Finally, from the perspective of revitalization, protection, and inheritance of intangible cultural heritage, the government should propose dialectical and integrated protection of intangible cultural heritage in South China while continuously enhancing its living and contemporary flavor.

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Section: Introductionmentioning

confidence: 99%

Spatial Differentiation of Intangible Cultural Heritage in South China and Its Influencing Factors

Cai

2022

Wireless Communications and Mobile Computing

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“…Deep learning is a branch of artificial intelligence that trains models by inputting data such as images, text, or sound, and enables models to learn to perform more complex classification tasks ( 30 ). Compared to traditional machine learning methods, deep learning has higher performance.…”

Section: Discussionmentioning

confidence: 99%

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

Zhang,

Li,

Lin

et al. 2023

Front. Med.

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ObjectivesTo explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.MethodsA total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.ResultsThe deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.ConclusionThis deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.

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“…In fact, with the advancement of technology, machine learning has found broad applications in many other fields, including but not limited to computer vision, pattern recognition, and audio-visual processing. For example, some studies have employed machine learning to address the problems of crowd counting and localization in computer vision ( 66 , 67 ). These studies utilized complex machine learning models, such as hybrid classical-quantum networks and audio-visual dual-stream frameworks, to process and analyze image and audio data for accomplishing specific tasks.…”

Section: Discussionmentioning

confidence: 99%

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

Li,

Dong,

Yuan

2023

Front. Oncol.

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BackgroundMalignant bone tumors are a type of cancer with varying malignancy and prognosis. Accurate diagnosis and classification are crucial for treatment and prognosis assessment. Machine learning has been introduced for early differential diagnosis of malignant bone tumors, but its performance is controversial. This systematic review and meta-analysis aims to explore the diagnostic value of machine learning for malignant bone tumors.MethodsPubMed, Embase, Cochrane Library, and Web of Science were searched for literature on machine learning in the differential diagnosis of malignant bone tumors up to October 31, 2022. The risk of bias assessment was conducted using QUADAS-2. A bivariate mixed-effects model was used for meta-analysis, with subgroup analyses by machine learning methods and modeling approaches.ResultsThe inclusion comprised 31 publications with 382,371 patients, including 141,315 with malignant bone tumors. Meta-analysis results showed machine learning sensitivity and specificity of 0.87 [95% CI: 0.81,0.91] and 0.91 [95% CI: 0.86,0.94] in the training set, and 0.83 [95% CI: 0.74,0.89] and 0.87 [95% CI: 0.79,0.92] in the validation set. Subgroup analysis revealed MRI-based radiomics was the most common approach, with sensitivity and specificity of 0.85 [95% CI: 0.74,0.91] and 0.87 [95% CI: 0.81,0.91] in the training set, and 0.79 [95% CI: 0.70,0.86] and 0.79 [95% CI: 0.70,0.86] in the validation set. Convolutional neural networks were the most common model type, with sensitivity and specificity of 0.86 [95% CI: 0.72,0.94] and 0.92 [95% CI: 0.82,0.97] in the training set, and 0.87 [95% CI: 0.51,0.98] and 0.87 [95% CI: 0.69,0.96] in the validation set.ConclusionMachine learning is mainly applied in radiomics for diagnosing malignant bone tumors, showing desirable diagnostic performance. Machine learning can be an early adjunctive diagnostic method but requires further research and validation to determine its practical efficiency and clinical application prospects.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023387057.

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RDC-SAL: Refine distance compensating with quantum scale-aware learning for crowd counting and localization

Cited by 13 publications

References 55 publications

Spatial Differentiation of Intangible Cultural Heritage in South China and Its Influencing Factors

Spatial Differentiation of Intangible Cultural Heritage in South China and Its Influencing Factors

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis

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