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
Intra-tumoural perfusion habitats showed prognostic value in glioblastoma patients
– Neuro-oncology
(2019)
21,
iv3
P14.129 Predicting glioblastoma invasion using multiparametric MRI and a bi-level machine learning approach
– Neuro-Oncology
(2019)
21,
iii99
(doi: 10.1093/neuonc/noz126.364)
14th Meeting of the European Association of Neuro-Oncology September 19–22, 2019 Lyon, France
– Neuro-oncology
(2019)
21,
NP
Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma
– European Radiology
(2019)
29,
235861
(doi: 10.1007/s00330-018-5984-z)
Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement
– Therapeutic Advances in Neurological Disorders
(2019)
12,
1756286419844664
(doi: 10.1177/1756286419844664)
Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival
– Radiother Oncol
(2019)
134,
17
(doi: 10.1016/j.radonc.2019.01.008)
Characterizing Tumor Invasiveness of Glioblastoma Using Multiparametric Magnetic Resonance Imaging
– J Neurosurg
(2019)
132,
1465
(doi: 10.3171/2018.12.JNS182926)
Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals
– Neoplasia (New York, N.Y.)
(2019)
21,
442
(doi: 10.1016/j.neo.2019.03.005)
Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma.
– European Radiology
(2019)
29,
4718
(doi: 10.1007/s00330-018-5984-z)
GANReDL: Medical Image Enhancement Using a Generative Adversarial Network with Real-Order Derivative Induced Loss Functions
(2019)
11766,
110
(doi: 10.1007/978-3-030-32248-9_13)
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