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
Phy-Diff: Physics-Guided Hourglass Diffusion Model for Diffusion MRI Synthesis
– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2024)
15002 LNCS,
345
(doi: 10.1007/978-3-031-72069-7_33)
Multi-objective Bayesian optimization with enhanced features for adaptively improved glioblastoma partitioning and survival prediction
– Comput Med Imaging Graph
(2024)
116,
102420
Knowledge-driven Subspace Fusion and Gradient Coordination for
Multi-modal Learning
(2024)
Deep learning enhancing guide RNA design for CRISPR/Cas12a‐based diagnostics
– iMeta
(2024)
3,
e214
(doi: 10.1002/imt2.214)
Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor
Microenvironment Subtype Prediction
(2024)
Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI
Synthesis
(2024)
Domain Game: Disentangle Anatomical Feature for Single Domain
Generalized Segmentation
(2024)
Cross-modal Diffusion Modelling for Super-resolved Spatial
Transcriptomics
(2024)
Global contextual representation via graph-transformer fusion for hepatocellular carcinoma prognosis in whole-slide images
– Comput Med Imaging Graph
(2024)
115,
102378
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