Office Info

924 Westwood Blvd., Suite 420, Room P

Jiayun Li


Jiayun's research focuses on developing weakly- or semi-supervised models to learn deep representations from large-scale whole slide image datasets, and combine histopathological features, imaging representation and clinical variable for disease progression prediction. Jiayun has also worked as a data scientist intern at Ancestry for image caption generation during summer 2018, and a software engineering intern in machine learning at Google for hotel photo analysis during summer 2019. 

Previously, Jiayun received a B.S. in Electrical Engineering at Fudan University in 2015. During that time, she worked as an undergraduate researcher on graphical models and their applications in Traditional Chinese Medicine at Adaptive Network and Control Lab.



Li J, Li W, Gertych A, Knudsen BS, Speier W, Arnold CW. An attention-based multi-resolution model for prostate whole slide image classification and localization. arXiv preprint arXiv:1905.13208. 2019 May 30.

Li W, Wang Z, Li J, Polson J, Speier W, Arnold CW. Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach. arXiv preprint arXiv:1905.06484. 2019 May 16.

Research Projects