Chao Li

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

Expectation-Maximization Regularised Deep Learning for Tumour Segmentation

C Li, W Huang, X Chen, Y Wei, L Zhang, J Zhang, S Price, CB Schonlieb

– 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

(2023)

2023-April,

(doi: 10.1109/isbi53787.2023.10230573)

Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients

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

– Brain

(2022)

146,

1714

(doi: 10.1093/brain/awac360)

Adaptive Unsupervised Learning with Enhanced Feature Representation for Intra-tumor Partitioning and Survival Prediction for Glioblastoma

Y Li, C Li, Y Wei, S Price, CB Schönlieb, X Chen

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

(2022)

12962 LNCS,

124

(doi: 10.1007/978-3-031-08999-2_10)

Predicting Isocitrate Dehydrogenase Mutation Status in Glioma Using Structural Brain Networks and Graph Neural Networks

Y Wei, Y Li, X Chen, CB Schönlieb, C Li, SJ Price

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

(2022)

12962,

140

(doi: 10.1007/978-3-031-08999-2_11)

Cerebrovascular risk factors impact brain phenotypes and cognitive function in healthy population

B Li, Y Wei, K Zhang, C-B Schönlieb, J Rudd, C Li

(2022)

(doi: 10.1101/2022.03.29.22273047)

Multi-modal learning for predicting the genotype of glioma

Y Wei, X Chen, L Zhu, L Zhang, C-B Schönlieb, SJ Price, C Li

(2022)

(link to publication)

Predicting conversion of mild cognitive impairment to Alzheimer's disease

Y Wei, SJ Price, C-B Schönlieb, C Li

(2022)

(doi: 10.48550/arxiv.2203.04725)

(link to publication)

Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading

L Zhang, Y Wei, Y Fu, S Price, C-B Schönlieb, C Li

(2022)

(link to publication)

Collaborative Learning of Images and Geometrics for Predicting Isocitrate Dehydrogenase Status of Glioma

Y Wei, C Li, X Chen, CB Schoinlieb, SJ Price

– Proceedings - International Symposium on Biomedical Imaging

(2022)

00,

(doi: 10.1109/ISBI52829.2022.9761407)

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)

Department of Applied Mathematics and Theoretical Physics

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