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
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,
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)
Cerebrovascular risk factors impact brain phenotypes and cognitive function in healthy population
(2022)
(doi: 10.1101/2022.03.29.22273047)
Predicting conversion of mild cognitive impairment to Alzheimer's disease
(2022)
(doi: 10.48550/arxiv.2203.04725)
Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image
Representations for Glioma Grading
(2022)
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 LNCS,
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)
2021.03.19.21253837
(doi: 10.1101/2021.03.19.21253837)
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