Dr Chao Li is a Senior 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 surrouding 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
Collaborative Learning of Images and Geometrics for Predicting Isocitrate Dehydrogenase Status of Glioma
– Proceedings - International Symposium on Biomedical Imaging
(2022)
2022-March,
1
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,
103
(DOI: 10.1007/978-3-030-88210-5_9)
Radiological tumor 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)
(DOI: 10.1101/2021.03.19.21253837)
Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients
(2021)
(DOI: 10.1101/2021.03.09.434656)
Quantifying Structural Connectivity in Brain Tumor Patients
(2021)
12907,
519
(DOI: 10.1007/978-3-030-87234-2_49)
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
– Sci Rep
(2020)
10,
9748
(DOI: 10.1038/s41598-020-66691-6)
Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps.
– The British journal of radiology
(2020)
93,
20190441
(DOI: 10.1259/bjr.20190441)
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