Jiayun Li

PhD Candidate
  • UCLA Bioengineering

Office: 924 Westwood Blvd, Suite 420, Room Q

E-Mail: jiayunli@g.ucla.edu


I received my B.S. in Electrical Engineering at Fudan University in 2015. During that time, I worked as an undergraduate researcher on graph models and its' applications in Traditional Chinese Medicine at Adaptive Network and Control Lab. My current research focuses on using weakly- or semi-supervised models to learn representations from large-scale whole slide image datasets, and combine histopathological features, imaging representation and clinical variable for disease progression prediction.


  1. Li W, Li J, Sarma KV, Ho KC, Shen S, Knudsen BS, Gertych A, Arnold CW. Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images. IEEE transactions on medical imaging [Internet]. IEEE; 2018;8(1):14429. Retrieved from: https://ieeexplore.ieee.org/abstract/document/8490855 DOI: 10.1109/TMI.2018.2875868. PDF
  2. Ing N, Ma Z, Li J, Salemi H, Arnold C, Knudsen BS, Gertych A. Semantic segmentation for prostate cancer grading by convolutional neural networks. Medical Imaging 2018: Digital Pathology. International Society for Optics and Photonics; 2018. p. 105811B.
  3. Li J, Speier W, Ho KC, Sarma KV, Gertych A, Knudsen BS, Arnold CW. An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies. Computerized Medical Imaging and Graphics. Elsevier; 2018;69:125–133.
  1. Li J, Sarma KV, Ho KC, Gertych A, Knudsen BS, Arnold CW. A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies. AMIA Annual Symposium Proceedings. American Medical Informatics Association; 2017. p. 1140.