CV

Education

• B.Eng. in Automatization, Hunan University, Changsha, China, 2011-2015
• M.Eng. in Biomedical Engineering, Hunan University, Changsha, China, 2015-2017
• Ph.D in Control science and engineering, Hunan University, Changsha, China, 2018-2022
• Visiting Ph.D student, University of Trento, Trento, Italy, 2021-2022

Skills

• Python
  • Pytorch
  • Tensorflow
• Matlab

Publications

Automatic Classification of Retinal Optical Coherence Tomography Images With Layer Guided Convolutional Neural Network

L. Huang, X. He, L. Fang, H. Rabbani, and X. Chen, “Automatic Classification of Retinal Optical Coherence Tomography Images With Layer Guided Convolutional Neural Network,” IEEE Signal Process. Lett., vol. 26, no. 7, pp. 1026–1030, Jul. 2019, doi: 10.1109/LSP.2019.2917779.

Retinal optical coherence tomography image classification with label smoothing generative adversarial network

X. He, L. Fang, H. Rabbani, X. Chen, and Z. Liu, “Retinal optical coherence tomography image classification with label smoothing generative adversarial network,” Neurocomputing, vol. 405, pp. 37–47, Sep. 2020, doi: 10.1016/j.neucom.2020.04.044.

Multi-Modal Retinal Image Classification With Modality-Specific Attention Network

X. He, Y. Deng, L. Fang, and Q. Peng, “Multi-Modal Retinal Image Classification With Modality-Specific Attention Network,” IEEE Trans. Med. Imaging, vol. 40, no. 6, pp. 1591–1602, Jun. 2021, doi: 10.1109/TMI.2021.3059956.

Intra- and Inter-Slice Contrastive Learning for Point Supervised OCT Fluid Segmentation

L. Huang, X. He, L. Fang, H. Rabbani, and X. Chen, “Automatic Classification of Retinal Optical Coherence Tomography Images With Layer Guided Convolutional Neural Network,” IEEE Signal Process. Lett., vol. 26, no. 7, pp. 1026–1030, Jul. 2019, doi: 10.1109/LSP.2019.2917779.

Self-supervised patient-specific features learning for OCT image classification

L. Fang, J. Guo, X. He, and M. Li, “Self-supervised patient-specific features learning for OCT image classification,” Med Biol Eng Comput, vol. 60, no. 10, pp. 2851–2863, Oct. 2022, doi: 10.1007/s11517-022-02627-8.