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Shangqi Gao

PhD


Postdoctoral Research Assistant in Computational Pathology

  • Identification of clinical needs
  • Implementation of image analysis solutions
  • Development of AI programmes.

Urological Cancer Pathology AI – Beyond Prostate

Biography

I earned my PhD degree in Statistics, supervised by Prof. Xiahai Zhuang, from The School of Data Science, Fudan University in 2022. Before that, I obtained my Master degree in Applied Mathematics from The School of Mathematics and Statistics, Wuhan University in 2018, and my Bachelor degree in Applied Mathematics from The Department of Mathematics, Northwestern Polytechnical University in 2015.

During my PhD,  I was focusing on Deep Image Decomposition and Reconstruction, especially for model interpretability, model generalizability, and unsupervised learning in photography and medical imaging. I am interested in inverse problems, computational imaging, and Bayesian deep learning.

Research

My research focuses on the “Urological Cancer Pathology AI – Beyond Prostate” project, with the aim to develop novel AI (artificial intelligence) histology-based clustering methods in various tissue types to landscape carcinoma in a quantitative and visual way. That objectively help histopathologists not only to diagnose, but also establish prognosis by categorising patients according to the aggression of their cancer.

I am developing new technology and contributing towards translating new AI solutions for pathology into the clinical environment. I am working on identification of clinical needs, through collaborations with pathologists at Cellular Pathology that can be addressed through quantitative imaging, and also implementation of image analysis solutions and development of AI programmes. I am doing research in The Verrill Pathology Research Group led by Prof. Clare Verrill and The Rittscher Quantitative Imaging Group led by Prof. Jens Rittscher.

Connections

I am working in The Verrill Pathology Group at Nuffield Department of Surgical Sciences, and The Rittscher Quantitative Imaging Group at Big Data Institute.