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
Predicting Isocitrate Dehydrogenase Mutation Status in Glioma Using Structural Brain Networks and Graph Neural Networks
– BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2021, PT I
(2022)
12962 LNCS,
140
(doi: 10.1007/978-3-031-08999-2_11)
Adaptive Unsupervised Learning with Enhanced Feature Representation for Intra-tumor Partitioning and Survival Prediction for Glioblastoma
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2022)
12962,
124
(doi: 10.1007/978-3-031-08999-2_10)
Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image
Representations for Glioma Grading
(2022)
Predicting conversion of mild cognitive impairment to Alzheimer's
disease
(2022)
Collaborative Learning of Images and Geometrics for Predicting Isocitrate Dehydrogenase Status of Glioma
– 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
(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 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)
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