Name: QIANG Yan
Gender: Male
Mobile Phone: (86) -186351686803
E-mail: qiangyan99@foxmail.com
Major Selection: Computer Application Technology
Research Directions: Medical Image Big Data, Advanced Artificial Intelligence, Cloud Computing Technology, Bioinformatics
Publication:
1. Hao R, Qiang Y, Yan X. Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector.[J]. Computational and Mathematical Methods in Medicine,2018,(2018-1-8), 2018, 2018(1):1-10.（SCI）
2. Qiang Y, Ge L, Hao R, et al. Automatic diagnosis of pulmonary nodules using a hierarchical extreme learning machine model[J]. International Journal of Bio-Inspired Computation, 2018, 11(3):192.（SCI）
3. Qiang Y, Ge L, Zhao X, et al. Pulmonary nodule diagnosis using dual‐modal supervised autoencoder based on extreme learning machine[J]. Expert Systems, 2017(11):e12224.（SCI）
4. Liao Xiaolei, Zhao Juanjuan, Jiao Cheng, Lei Lei, Qiang Yan, Cui Qiang. A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest[J]. Plos One, 2016, 11(8). (SCI）
5. Pan L, Qiang Y, Yuan J, et al. Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods[J]. BioMed Research International, 2016, 2016:1-10. (SCI)
6. Qiang, Y., Zhang, X., Ji, G., & Zhao, J. (2015). Automated Lung Nodule Segmentation Using an Active Contour Model Based on PET/CT Images.Journal of Computational and Theoretical Nanoscience, 12(8), 1972-1976.（SCI）
7. Qiang, Y., Ji, G., Han, X., Zhao, J., Liao, X., & Cui, Z. (2015). Coarse-to-Fine Lung Segmentation in Computed Tomography Images. Journal of Computational and Theoretical Nanoscience, 12(2), 330-334. （SCI）
8. Qiang Y, Pei B, Wei W, et al. An efficient cluster head selection approach for collaborative data processing in wireless sensor networks[J]. International Journal of Distributed Sensor Networks, 2015, 2015: 132.（SCI）
9. Qiang Y, Pei B, Wu W, et al. Improvement of path analysis algorithm in social networks based on HBase[J]. Journal of Combinatorial Optimization, 2014, 28(3): 588-599.（SCI）
10. Zhao, J., Ma, R., Qiang, Y., & Cui, Z. (2015). Solitary Pulmonary Nodule Segmentation Based on the Rolling Ball Method. Journal of Computational and Theoretical Nanoscience, 12(8), 1977-1983. （SCI）
11. Zhao, J., Ji, G., Qiang, Y., Han, X., Pei, B., & Shi, Z. (2015). A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm. PloS one, 10(4). (SCI)
12. Zhao,JJ; Wu,WL; Zhang, XL; Qiang, Y; Liu, T (Liu, Tao); Wu, LD. A short-term trend prediction model of topic over Sina Weibo dataset, JOURNAL OF COMBINATORIAL OPTIMIZATION,Volume: 28,Issue: 3,Pages: 613-625,Special Issue: SI,DOI: 10.1007/s10878-013-9674-0,Published: OCT 2014,IDS Number: AO6YL.(SCI)
13. Wei, Wei; Qiang, Yan; Zhang, Jing. A Bijection between Lattice-Valued Filters and Lattice-Valued Congruences in Residuated Lattices．MATHEMATICAL PROBLEMS IN ENGINEERING, 2013.(SCI)
14. Cui, Q., Qiang, Z., Zhao, J., Qiang, Y., & Liao, X. (2017). A 3D Segmentation Method for Pulmonary Nodule Image Sequences based on Supervoxels and Multimodal Data. International Journal of Performability Engineering, 13(5).:682-696. (EI)
15. Zhang, T., Zhao, J., Luo, J., & Qiang, Y. (2017). Deep Belief Network for Lung Nodules Diagnosed in CT Imaging. International Journal of Performability Engineering, 13(8). (EI)
16. He, N., Zhang, X., Zhao, J., Zhao, H., & Qiang, Y. Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity[C]// International Conference on Digital Image Processing. 2017:104202G. (EI)
17. Xiao X, Qiang Y, Zhao J, et al. A Deep Learning Model of Automatic Detection of Pulmonary Nodules Based on Convolution Neural Networks (CNNs)[C]// Bio-Inspired Computing - Theories and Applications. Springer Singapore, 2016:349-361. (EI)
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