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Shaoxiong Liu
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2020 – today
- 2021
- [j5]Yang Dong, Jiachen Wan, Lu Si, Yixin Meng, Yanmin Dong, Shaoxiong Liu, Honghui He, Hui Ma:
Deriving Polarimetry Feature Parameters to Characterize Microstructural Features in Histological Sections of Breast Tissues. IEEE Trans. Biomed. Eng. 68(3): 881-892 (2021)
2010 – 2019
- 2019
- [j4]Yuexiang Li, Xinpeng Xie, Linlin Shen, Shaoxiong Liu:
Reverse active learning based atrous DenseNet for pathological image classification. BMC Bioinform. 20(1): 445:1-445:15 (2019) - 2018
- [c3]Yuexiang Li, Xinpeng Xie, Shaoxiong Liu, Xuechen Li, Linlin Shen:
GT-Net: A Deep Learning Network for Gastric Tumor Diagnosis. ICTAI 2018: 20-24 - [i2]Ling Zhang, Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, Jianhua Yao:
DeepPap: Deep Convolutional Networks for Cervical Cell Classification. CoRR abs/1801.08616 (2018) - [i1]Yuexiang Li, Xinpeng Xie, Linlin Shen, Shaoxiong Liu:
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification. CoRR abs/1807.02420 (2018) - 2017
- [j3]Ling Zhang, Hui Kong, Shaoxiong Liu, Tianfu Wang, Siping Chen, Milan Sonka:
Graph-based segmentation of abnormal nuclei in cervical cytology. Comput. Medical Imaging Graph. 56: 38-48 (2017) - [j2]Ling Zhang, Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, Jianhua Yao:
DeepPap: Deep Convolutional Networks for Cervical Cell Classification. IEEE J. Biomed. Health Informatics 21(6): 1633-1643 (2017) - 2015
- [c2]Ling Zhang, Shaoxiong Liu, Tianfu Wang, Siping Chen, Milan Sonka:
Improved segmentation of abnormal cervical nuclei using a graph-search based approach. Digital Pathology 2015: 94200W - 2014
- [j1]Ling Zhang, Hui Kong, Chien Ting Chin, Shaoxiong Liu, Zhi Chen, Tianfu Wang, Siping Chen:
Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts. Comput. Medical Imaging Graph. 38(5): 369-380 (2014) - [c1]Ling Zhang, Hui Kong, Chien Ting Chin, Shaoxiong Liu, Tianfu Wang, Siping Chen:
Automated segmentation of abnormal cervical cells using global and local graph cuts. ISBI 2014: 485-488
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