
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
– 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
– Nature Machine Intelligence
(2021)
3,
787
(doi: 10.1038/s42256-021-00377-0)
Quantifying structural connectivity in brain tumor patients
(2021)
2021.03.19.21253837
(doi: 10.1101/2021.03.19.21253837)
Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients
(2021)
2021.03.09.434656
(doi: 10.1101/2021.03.09.434656)
Expectation-Maximization Regularized Deep Learning for Weakly Supervised
Tumor Segmentation for Glioblastoma
(2021)
Quantifying Structural Connectivity in Brain Tumor Patients
(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
(2020)
Glioblastoma surgery related emotion recognition deficits are associated with right cerebral hemisphere tract changes
– 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.
– 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.
– Scientific reports
(2020)
10,
9748
(doi: 10.1038/s41598-020-66691-6)
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