Nonparametric Bayesian Prior Inducing Deep Network for Automatic Detection of Cognitive Status

Wu, Edmond Q.; Hu, Dewen; Deng, Ping-Yu; Tang, Zhi-Ri; Cao, Yulian; Zhang, Wen Ming; Zhu, Limin; Ren, He

doi:10.1109/tcyb.2020.2977267

Cited by 49 publications

(29 citation statements)

References 36 publications

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“…Owing to the development of detection technology, the available samples have increased explosively in terms of dimension. When machine learning algorithms are applied to these high-dimensional data [ 33 , 34 ], dimension curse becomes a crucial issue to resolve, which is especially severe in bioinformatics [ 35 , 36 ].…”

Section: Machine Learning Related Proceduresmentioning

confidence: 99%

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Liu

Chen

Petinrin

et al. 2021

Life

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With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

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Section: Machine Learning Related Proceduresmentioning

confidence: 99%

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Liu

Chen

Petinrin

et al. 2021

Life

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“…Generally speaking, deep reinforcement learning is the product of combining deep learning and reinforcement learning [5], [6]. Deep [7], such as autoencoder [8], [9], deep belief networks [10]- [12] (including Boltzmann machines) and Generative Adversarial Networks (GAN) [13], [14]. Deep supervised learning with labeled samples has achieved great success on Euclidean data and sequential data with Convolution Neural Network (CNN) [15]- [17] and Recurrent Neural Network (RNN) [18], [19], respectively, and supervised learning directly on various non-Euclidean data structure with Bayesian deep learning also arouses much attention [20].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Lane Marking Detection: A Survey

Zhang

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.

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“…Unfortunately, the mechanism of C5P remains controversial, and there is a lack of a reliable prediction tool so far. In recent years, machine learning technology has received growing attention in medicine and healthcare [12][13][14]. The support vector machine (SVM), proposed by VAPNIK in 1997, is a linear and nonlinear classification method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the risk of C5 palsy after posterior laminectomy and fusion with cervical myelopathy using a support vector machine: an analysis of 184 consecutive patients

Wang

Tang

et al. 2021

J Orthop Surg Res

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Background This study aimed to predict C5 palsy (C5P) after posterior laminectomy and fusion (PLF) with cervical myelopathy (CM) from routinely available variables using a support vector machine (SVM) method. Methods We conducted a retrospective investigation based on 184 consecutive patients with CM after PLF, and data were collected from March 2013 to December 2019. Clinical and imaging variables were obtained and imported into univariable and multivariable logistic regression analyses to identify risk factors for C5P. According to published reports and clinical experience, a series of variables was selected to develop an SVM machine learning model to predict C5P. The accuracy (ACC), area under the receiver operating characteristic curve (AUC), and confusion matrices were used to evaluate the performance of the prediction model. Results Among the 184 consecutive patients, C5P occurred in 26 patients (14.13%). Multivariate analyses demonstrated the following 4 independent factors associated with C5P: abnormal electromyogram (odds ratio [OR] = 7.861), JOA recovery rate (OR = 1.412), modified Pavlov ratio (OR = 0.009), and presence of C4–C5 foraminal stenosis (OR = 15.492). The SVM model achieved an area under the receiver operating characteristic curve (AUC) of 0.923 and an ACC of 0.918. Additionally, the confusion matrix showed the classification results of the discriminant analysis. Conclusions The designed SVM model presented satisfactory performance in predicting C5P from routinely available variables. However, future external validation is needed.

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Nonparametric Bayesian Prior Inducing Deep Network for Automatic Detection of Cognitive Status

Cited by 49 publications

References 36 publications

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Deep Learning in Lane Marking Detection: A Survey

Prediction of the risk of C5 palsy after posterior laminectomy and fusion with cervical myelopathy using a support vector machine: an analysis of 184 consecutive patients

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