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Author

Danfeng Xie

Other affiliations: Chinese Academy of Sciences
Bio: Danfeng Xie is an academic researcher from Temple University. The author has contributed to research in topics: Video tracking & Motion estimation. The author has an hindex of 6, co-authored 10 publications receiving 158 citations. Previous affiliations of Danfeng Xie include Chinese Academy of Sciences.

Papers
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Journal ArticleDOI
01 Feb 2017
TL;DR: This study not only reviews typical deep learning algorithms in computer vision and signal processing but also provides detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection.
Abstract: Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks.

99 citations

Journal ArticleDOI
TL;DR: A DL-based ASL MRI denoising algorithm (DL-ASL) that was constructed using convolutional neural networks with dilated convolution and wide activation residual blocks to explicitly take the inter-voxel correlations into account, and preserve spatial resolution of input image during model learning is proposed and validated.

49 citations

Proceedings ArticleDOI
Danfeng Xie1, Li Bai1
01 Dec 2015
TL;DR: A hierarchical deep neural network for diagnosing the faults on the Tennessee-Eastman process (TEP) and demonstrates that the method outperforms the SNN and DOHANN methods.
Abstract: This paper proposes a hierarchical deep neural network (HDNN) for diagnosing the faults on the Tennessee-Eastman process (TEP). The TEP process is a benchmark simulation model for evaluating process control and monitoring method. A supervisory deep neural network is trained to categorize the whole faults into a few groups. For each group of faults, a special deep neural network which is trained for the particular group is triggered for further diagnosis. The training and test data is generated from the Tennessee Eastman process simulation. The performance of the proposed method is evaluated and compared to single neural network (SNN) and duty-oriented hierarchical artificial neural network (DOHANN) methods. The results of experiment demonstrate that our method outperforms the SNN and DOHANN methods.

42 citations

Journal ArticleDOI
TL;DR: A novel deep learning (DL)‐based algorithm armed with wide activation neural network blocks is proposed to address the issues of prolonged acquisition time and low signal‐to‐noise‐ratio (SNR) in GluCEST.
Abstract: Purpose Glutamate weighted Chemical Exchange Saturation Transfer (GluCEST) MRI is a noninvasive technique for mapping parenchymal glutamate in the brain. Because of the sensitivity to field (B0 ) inhomogeneity, the total acquisition time is prolonged due to the repeated image acquisitions at several saturation offset frequencies, which can cause practical issues such as increased sensitivity to patient motions. Because GluCEST signal is derived from the small z-spectrum difference, it often has a low signal-to-noise-ratio (SNR). We proposed a novel deep learning (DL)-based algorithm armed with wide activation neural network blocks to address both issues. Methods B0 correction based on reduced saturation offset acquisitions was performed for the positive and negative sides of the z-spectrum separately. For each side, a separate deep residual network was trained to learn the nonlinear mapping from few CEST-weighted images acquired at different ppm values to the one at 3 ppm (where GluCEST peaks) in the same side of the z-spectrum. Results All DL-based methods outperformed the "traditional" method visually and quantitatively. The wide activation blocks-based method showed the highest performance in terms of Structural Similarity Index (SSIM) and peak signal-to-noise ratio (PSNR), which were 0.84 and 25dB respectively. SNR increases in regions of interest were over 8dB. Conclusion We demonstrated that the new DL-based method can reduce the entire GluCEST imaging time by ˜50% and yield higher SNR than current state-of-the-art.

22 citations

Journal ArticleDOI
TL;DR: ASL CBF can be substantially improved using prior-guided and slice-wise outlier rejection and the proposed method will benefit the ever since increasing ASL user community for both clinical and scientific brain research.

18 citations


Cited by
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Journal ArticleDOI
TL;DR: A Deep Convolutional Neural Network trained on the ‘big data’ ImageNet database is employed to automatically detect cracks in Hot-Mix Asphalt and Portland Cement Concrete surfaced pavement images that also include a variety of non-crack anomalies and defects.

655 citations

Journal ArticleDOI
TL;DR: A fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed for chemical process fault diagnosis and the benchmark Tennessee Eastman (TE) process is utilized to verify the outstanding performance.

316 citations

Journal ArticleDOI
TL;DR: Overall, multi-modal fusion shows significant benefits in clinical diagnosis and neuroscience research and widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

180 citations

Journal ArticleDOI
01 Jul 2021
TL;DR: A comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles examines the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations.
Abstract: This article presents a comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles. Unlike existing review papers, we examine the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations. Deep learning is one potential solution for object detection and scene perception problems, which can enable algorithm-driven and data-driven cars. In this article, we aim to bridge the gap between deep learning and self-driving cars through a comprehensive survey. We begin with an introduction to self-driving cars, deep learning, and computer vision followed by an overview of artificial general intelligence. Then, we classify existing powerful deep learning libraries and their role and significance in the growth of deep learning. Finally, we discuss several techniques that address the image perception issues in real-time driving, and critically evaluate recent implementations and tests conducted on self-driving cars. The findings and practices at various stages are summarized to correlate prevalent and futuristic techniques, and the applicability, scalability and feasibility of deep learning to self-driving cars for achieving safe driving without human intervention. Based on the current survey, several recommendations for further research are discussed at the end of this article.

175 citations