Yuqi FANG (房钰棋)

Yuqi FANG (房钰棋)

Assistant Professor

Biography

Dr. Fang is an Assistant Professor at School of Intelligence Science and Technology, Nanjing University. Before joining Nanjing University, She obtained Ph.D. from The Chinese University of Hong Kong (CUHK) in 2020, supervised by Prof. Kai-Yu Tong (AIMBE Fellow). She previously worked as a postdoctoral research associate at The University of North Carolina at Chapel Hill (2021-2024), supervised by Prof. Mingxia Liu. She also interned at Tencent AI Lab (2019-2020) under the mentorship of Dr. Jianhua Yao (AIMBE Fellow).

Research Interests

Focusing on interdisciplinary field of artificial intelligence (AI) and medical data analysis, aiming to develop intelligent systems that advance clinical decision-making and healthcare, including:

  • Automated disease diagnosis
  • Multi-modality data analysis
  • Model explainability
  • Multi-site data adaptation

News

#f03c15 课题组招收2025年保研/推免的硕士研究生4人，直博生/考核制博士研究生2人；招收2024年考研的硕士研究生2人，欢迎与我联系！

  • [08/2024] One paper on source-free domain adaptation was accepted in Pattern Recognition.
  • [05/2024] One paper on multimodal fusion was early accepted in MICCAI.
  • [05/2024] Join Nanjing University, a new chapter starts!
  • [03/2024] One paper on source-free domain adaptation was accepted in Neural Networks.
  • [02/2024] One paper on interpretable fMRI analysis was accepted in IEEE TBME.
  • [10/2023] One paper on interpretable fMRI analysis was accepted in MICCAI.
  • [05/2023] One paper on fMRI adaptation was accepted in Human Brain Mapping.
  • [03/2023] One paper on fundus lesion segmentation was accepted in Neurocomputing.
  • [02/2023] One paper on fMRI adaptation was accepted in Medical Image Analysis.
  • [10/2022] One paper on microscopy image classification was accepted in Genomics, Proteomics & Bioinformatics.
  • [10/2022] One paper on structural MRI adaptation was accepted in MICCAI.
  • [09/2022] One paper on PET/CT analysis was accepted in MLMI.
  • [06/2022] One paper on pathological complete response prediction was accepted in Frontiers in Oncology.

Selected Publications

Journals

  • Fang, Y., Wang, M., Potter, G. G., & Liu, M. (2023). Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification. Medical Image Analysis, 84, 102707. (IF: 13.8, JCR Q1)
  • Fang, Y., Wu, J., Wang, Q., Qiu, S., Bozoki, A., & Liu, M. (2024). Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis. Pattern Recognition, 110912. (IF: 7.5, JCR Q1)
  • Fang, Y., Yap, P. T., Lin, W., Zhu, H., & Liu, M. (2024). Source-free unsupervised domain adaptation: A survey. Neural Networks, 106230. (IF: 7.8, JCR Q1)
  • Wang, X.*, Fang, Y.*, Yang, S., Zhu, D., Wang, M., Zhang, J., … & Han, X. (2021). A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images. Medical Image Analysis, 68, 101914. (IF: 13.8, JCR Q1)
  • Fang, Y., Potter, G. G., Wu, D., Zhu, H., & Liu, M. (2023). Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification. Human Brain Mapping, 44(11), 4256-4271. (IF: 5.4, JCR Q1)
  • Fang, Y., Zhu, D., Yao, J., Yuan, Y., & Tong, K. Y. (2020). ABC-Net: Area-boundary constraint network with dynamical feature selection for colorectal polyp segmentation. IEEE Sensors Journal, 21(10), 11799-11809. (IF: 4.3, JCR Q1)
  • Wang, X.*, Fang, Y.*, Yang, S., Zhu, D., Wang, M., Zhang, J., Tong, K. Y., & Han, X. (2023). CLC-Net: contextual and local collaborative network for lesion segmentation in diabetic retinopathy images. Neurocomputing, 527, 100-109. (IF: 7.1, JCR Q2)
  • Wong, W. W.*, Fang, Y.*, Chu, W. C., Shi, L., & Tong, K. Y. (2019). What kind of brain structural connectivity remodeling can relate to residual motor function after stroke?. Frontiers in Neurology, 10, 1111. (IF: 4.1, JCR Q2)
  • Yang, S.*, Shen, T.*, Fang, Y.*, Wang, X., Zhang, J., Yang, W., … & Han, X. (2023). DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning. Genomics, Proteomics & Bioinformatics, 20(5), 989-1001. (IF: 6.4, JCR Q1)

Conferences

  • Fang, Y., Zhu, D., Zhou, N., Liu, L., & Yao, J. (2021, September). PiPo-Net: A semi-automatic and polygon-based annotation method for pathological images. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2978-2984). IEEE.
  • Fang, Y., Chen, C., Yuan, Y., & Tong, K. Y. (2019, October). Selective feature aggregation network with area-boundary constraints for polyp segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 302-310). Springer, Cham.
  • Fang, Y., Daniel Oldan, J., Lin, W., Parke Schrank, T., Gray Yarbrough, W., Isaeva, N., & Liu, M. (2022). Prediction of HPV-associated genetic diversity for squamous cell carcinoma of head and neck cancer based on 18F-FDG PET/CT. In International Workshop on Machine Learning in Medical Imaging (pp. 358-366). Springer, Cham.
  • Fang, Y., Chen, S., Wang, X., Leung, K. W., Wang, X., & Tong, K. Y. (2018, July). Real-time electromyography-driven functional electrical stimulation cycling system for chronic stroke rehabilitation. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2515-2518). IEEE.

(* means co-first authors)

Journal Reviews

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Medical Image Analysis
  • IEEE Transactions on Medical Imaging
  • Pattern Recognition
  • Neural Networks

Selected Awards

  • Outstanding Tutor Award, The Chinese University of Hong Kong, 2020
  • First Prize in International Brain Computer Interface Hackathon, Honolulu, USA, 2018
  • Excellent Graduate of Liaoning Province, Northeastern University, 2016
  • National Scholarship (2 times), Ministry of Education of P.R.China, 2014 & 2015
  • Meritorious Winner in Mathematical Contest In Modeling, COMAP, USA, 2015