Chao Li | Department of Applied Mathematics and Theoretical Physics

skip to content

Dr Chao Li is a Principal Research Fellow with expertise in both healthcare and AI innovation, with comprehensive experience in developing image-based AI and multi-omics approaches to model neurological diseases. Dr Li is particularly interested in developing cost-effective AI models and translating these models into healthcare management to promote personalised medicine. His research is surrounding the below themes: 1. Image-based AI for precision mental health. 2. Image-based AI for precision surgical and interventional oncology. 3. Multi-omics AI for disease characterisation and precision medicine. 4. Efficacy and safety assessment of AI innovations for clinical translation and enterprise.

Publications

BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dementia Classification

C Li, Y Wei, X Chen, CB Schönlieb

– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

(2021)

13003 LNCS,

103

(doi: 10.1007/978-3-030-88210-5_9)

Radiological tumour classification across imaging modality and histology

J Wu, C Li, M Gensheimer, S Padda, F Kato, H Shirato, Y Wei, C-B Schönlieb, SJ Price, D Jaffray, J Heymach, JW Neal, BW Loo, H Wakelee, M Diehn, R Li

– Nature Machine Intelligence

(2021)

3,

787

(doi: 10.1038/s42256-021-00377-0)

Quantifying structural connectivity in brain tumor patients

Y Wei, C Li, SJ Price

(2021)

2021.03.19.21253837

(doi: 10.1101/2021.03.19.21253837)

Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

Y Wei, C Li, Z Cui, R Mayrand, J Zou, ALKC Wong, R Sinha, T Matys, C-B Schönlieb, SJ Price

(2021)

2021.03.09.434656

(doi: 10.1101/2021.03.09.434656)

Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma

C Li, W Huang, X Chen, Y Wei, SJ Price, C-B Schönlieb

(2021)

(link to publication)

Quantifying Structural Connectivity in Brain Tumor Patients

Y Wei, C Li, SJ Price

(2021)

12907 LNCS,

519

(doi: 10.1007/978-3-030-87234-2_49)

Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients

Y Li, C Li, S Price, C-B Schönlieb, X Chen

(2020)

(link to publication)

Glioblastoma surgery related emotion recognition deficits are associated with right cerebral hemisphere tract changes

R Sinha, A Dijkshoorn, C Li, T Manly, S Price

– Brain Communications

(2020)

2,

fcaa169

(doi: 10.1093/braincomms/fcaa169)

Publisher Correction: A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics.

J-L Yan, C Li, A van der Hoorn, NR Boonzaier, T Matys, SJ Price

– Scientific reports

(2020)

10,

13808

(doi: 10.1038/s41598-020-70346-x)

A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics.

J-L Yan, C Li, A van der Hoorn, NR Boonzaier, T Matys, SJ Price

– Scientific reports

(2020)

10,

9748

(doi: 10.1038/s41598-020-66691-6)