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
Knowledge-driven Subspace Fusion and Gradient Coordination for
Multi-modal Learning
(2024)
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)
Brain tumour microstructure is associated with post-surgical cognition.
– Scientific Reports
(2024)
14,
5646
(doi: 10.1038/s41598-024-55130-5)
Multi-modal learning for predicting the genotype of glioma
– IEEE Trans Med Imaging
(2023)
42,
1
(doi: 10.1109/tmi.2023.3244038)
G-CNN: Adaptive Geometric Convolutional Neural Networks for MRI-Based Skull Stripping
– Lecture Notes in Computer Science
(2023)
14243,
21
(doi: 10.1007/978-3-031-45087-7_3)
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-contrast MRI Super-Resolution
– Lecture Notes in Computer Science
(2023)
14229 LNCS,
387
(doi: 10.1007/978-3-031-43999-5_37)
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