Institution
Nanjing University of Science and Technology
Education•Nanjing, China•
About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.
Papers published on a yearly basis
Papers
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University of Barcelona1, German Cancer Research Center2, Chinese Academy of Sciences3, Nanjing University of Science and Technology4, Polytechnic University of Valencia5, University of California, Berkeley6, University of Oxford7, Nile University8, Technische Universität München9, King's College London10, Eindhoven University of Technology11, University of Alberta12, University of Edinburgh13, Shenzhen University14, Shanghai Jiao Tong University15, Fudan University16, Autonomous University of Barcelona17, University of Naples Federico II18, McGill University19, Queen Mary University of London20
TL;DR: The results of the MICCAI 2020 Challenge on generalizable deep learning for cardiac segmentation are presented in this article, where 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques.
Abstract: The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.
127 citations
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TL;DR: In this article, the authors characterised dynamics of jumps in oil market price using high frequency data from three perspectives: the probability (or intensity) of jump occurrence, the sign (e.g. positive or negative) of jumps, and the concurrence with stock market jumps.
127 citations
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TL;DR: In this article, the microstructure and mechanical properties of maraging steel parts were investigated and the micro-hardness and tensile strength of the as deposited alloy reduced from the bottom to the top due to the transient thermal cycling, which resulted in partial aging and non-uniform formation of intermetallic compounds along the building direction.
127 citations
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TL;DR: In this paper, the grain size effect on deformation twinning and detwinning in face-centered cubic (fcc) metals is systematically overviewed, and an analytical model based on observed deformation physics in nc metals, i.e., grain boundary emission of dislocations, provides an explanation of the observed optimum grain size for twinning.
Abstract: This article systematically overviews the grain size effect on deformation twinning and detwinning in face-centered cubic (fcc) metals. With decreasing grain size, coarse-grained fcc metals become more difficult to deform by twinning, whereas nanocrystalline (nc) fcc metals first become easier to deform by twinning and then become more difficult, exhibiting an optimum grain size for twinning. The transition in twinning behavior from coarse-grained to nc fcc metals is caused by the change in deformation mechanisms. An analytical model based on observed deformation physics in nc metals, i.e., grain boundary emission of dislocations, provides an explanation of the observed optimum grain size for twinning in nc fcc metals. The detwinning process is caused by the interaction between dislocations and twin boundaries. Under a certain deformation condition, there exists a grain size range where the twinning process dominates over the detwinning process to produce the highest density of twins.
127 citations
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TL;DR: A deep convolutional neural network VGG19-based novel feature extraction framework is proposed and closed-form metric learning is applied to measure the similarity between the query image and database images and outperforms the state-of-the-art CBIR systems on the CE-MRI dataset.
Abstract: This paper presents an automatic content-based image retrieval (CBIR) system for brain tumors on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key challenge in CBIR systems for MR images is the semantic gap between the low-level visual information captured by the MRI machine and the high-level information perceived by the human evaluator. The traditional feature extraction methods focus only on low-level or high-level features and use some handcrafted features to reduce this gap. It is necessary to design a feature extraction framework to reduce this gap without using handcrafted features by encoding/combining low-level and high-level features. Deep learning is very powerful for feature representation that can depict low-level and high-level information completely and embed the phase of feature extraction in self-learning. Therefore, we propose a deep convolutional neural network VGG19-based novel feature extraction framework and apply closed-form metric learning to measure the similarity between the query image and database images. Furthermore, we adopt transfer learning and propose a block-wise fine-tuning strategy to enhance the retrieval performance. The extensive experiments are performed on a publicly available CE-MRI dataset that consists of three types of brain tumors (i.e., glioma, meningioma, and pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal views. Our method is more generic, as we do not use any handcrafted features; it requires minimal preprocessing, tested as robust on fivefold cross-validation, can achieve a fivefold mean average precision of 96.13%, and outperforms the state-of-the-art CBIR systems on the CE-MRI dataset.
127 citations
Authors
Showing all 31818 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jian Yang | 142 | 1818 | 111166 |
Liming Dai | 141 | 781 | 82937 |
Hui Li | 135 | 2982 | 105903 |
Jian Zhou | 128 | 3007 | 91402 |
Shuicheng Yan | 123 | 810 | 66192 |
Zidong Wang | 122 | 914 | 50717 |
Xin Wang | 121 | 1503 | 64930 |
Xuan Zhang | 119 | 1530 | 65398 |
Zhenyu Zhang | 118 | 1167 | 64887 |
Xin Li | 114 | 2778 | 71389 |
Zeshui Xu | 113 | 752 | 48543 |
Xiaoming Li | 113 | 1932 | 72445 |
Chunhai Fan | 112 | 702 | 51735 |
H. Vincent Poor | 109 | 2116 | 67723 |
Qian Wang | 108 | 2148 | 65557 |