• Quantitative Image Analysis using Machine Learning and Artificial Intelligence

•  Longevity and Health Optimisation using Big Data Health Analytics

• Advanced Cancer Imaging (Prostate, Liver, Nasopharyngeal and Lung Carcinoma)

• Spectral CT

Brief Bio

Dr Vardhanabhuti completed his medical degree at Guy's, King's and St Thomas' School of Medicine in London, UK in 2005. He had subsequent training in London, Oxford, Plymouth, Exeter, and completed his Radiology training at Imperial College London, UK whilst also completing a PhD during his residency.

He has active interests in data science, machine learning, and artificial intelligence while engaging with various research projects relating to medical imaging, with the goal of early clinical translation to benefits patients. He has wide-ranging projects in the AI space including the use of quantitative radiomics in cancer prognostication, using deep learning model as a tool for automatic segmentation and cancer detection, big data using electronic patient records, etc. More recently he has been engaged in several projects applying machine learning and artificial intelligence techniques in tackling ageing and longevity pursuits.  

He is passionate about using technology in education. He has previously served as a Microsoft Cloud Research Software Fellow and an Amazon Faculty Ambassador in AWS Educate Cloud Ambassador Program, as well as Vice Chair for community engagement in the International Society of Radiology.

Current Reviewer:
European Radiology – Section Editor and Scientific Editorial Board “Computed Tomography” section

Previous/Ad Hoc Reviewer:
Investigative Radiology, European Radiology, British Journal of Radiology, Hong Kong Medical Journal, Korean Journal of Radiology, Scientific Report, npjDigital Medicine, Lancet Digital Health, Diagnostics, Clinical Radiology

Journal Papers (Most recent 40 Journal Papers (n=88) )

• Bhattarai, A., Lok, J.G.-T., Sun, H., Vardhanabhuti, V., 2022. Computed Tomography of Cartilage: An Exploration of Novel Cationic Bismuth Contrast Agent. Ann Biomed Eng. https://doi.org/10.1007/s10439-022-03110-z
• Huang, F., Xia, P., Vardhanabhuti, V., Hui, S.-K., Lau, K.-K., Ka-Fung Mak, H., Cao, P., 2023. Semisupervised white matter hyperintensities segmentation on MRI. Hum Brain Mapp 44, 1344–1358. https://doi.org/10.1002/hbm.26109
• Lian, J., Deng, J., Hui, E.S., Koohi-Moghadam, M., She, Y., Chen, C., Vardhanabhuti, V*., 2022. Early stage NSCLS patients’ prognostic prediction with multi-information using transformer and graph neural network model. Elife 11, e80547. https://doi.org/10.7554/eLife.80547
• Luo, L., Chen, H., Xiao, Y., Zhou, Y., Wang, X., Vardhanabhuti, V., Wu, M., Han, C., Liu, Z., Fang, X.H.B., Tsougenis, E., Lin, H., Heng, P.-A., 2022. Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study. Radiol Artif Intell 4, e210299. https://doi.org/10.1148/ryai.210299
• Mui, A.W.L., Lee, A.W.M., Ng, W.T., Lee, V.H.F., Vardhanabhuti, V., Man, S.S.Y., Chua, D.T.T., Guan, X.Y., 2022. Correlations of tumour permeability parameters with apparent diffusion coefficient in nasopharyngeal carcinoma. Phys Imaging Radiat Oncol 24, 30–35. https://doi.org/10.1016/j.phro.2022.09.001
• Wu, W., Yu, H., Liu, F., Zhang, J., Vardhanabhuti, V., 2022. Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization. Comput Biol Med 151, 106080. https://doi.org/10.1016/j.compbiomed.2022.106080
• Zhao, D., Homayounfar, M., Zhen, Z., Wu, M.-Z., Yu, S.Y., Yiu, K.-H., Vardhanabhuti, V., Pelekos, G., Jin, L., Koohi-Moghadam, M., 2022. A Multimodal Deep Learning Approach to Predicting Systemic Diseases from Oral Conditions. Diagnostics (Basel) 12, 3192. https://doi.org/10.3390/diagnostics12123192
• Chan, S.-K., O’Sullivan, B., Huang, S.H., Chau, T.-C., Lam, K.-O., Chan, S.-Y., Tong, C.-C., Vardhanabhuti, V., Kwong, D.L.-W., Ng, C.-Y., Leung, T.-W., Luk, M.-Y., Lee, A.W.-M., Choi, H.C.-W., Lee, V.H.-F., 2022. An Exploratory Study of Refining TNM-8 M1 Categories and Prognostic Subgroups Using Plasma EBV DNA for Previously Untreated De Novo Metastatic Nasopharyngeal  Carcinoma. Cancers (Basel) 14. https://doi.org/10.3390/cancers14081923
• Huang, F., Lian, J., Ng, K.-S., Shih, K., Vardhanabhuti, V*., 2022. Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network. Diagnostics (Basel) 12. https://doi.org/10.3390/diagnostics12061390
• Lian, J., Long, Y., Huang, F., Ng, K.S., Lee, F.M.Y., Lam, D.C.L., Fang, B.X.L., Dou, Q., Vardhanabhuti, V., 2022. Imaging-Based Deep Graph Neural Networks for Survival Analysis in Early Stage Lung Cancer Using CT: A Multicenter Study. Front Oncol 12, 868186. https://doi.org/10.3389/fonc.2022.868186
• Shabani, S., Homayounfar, M., Vardhanabhuti, V., Nikouei Mahani, M.-A., Koohi-Moghadam, M., 2022. Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning. Comput Biol Med 149, 106033. https://doi.org/10.1016/j.compbiomed.2022.106033
• Wu, W., Hu, D., Cong, W., Shan, H., Wang, S., Niu, C., Yan, P., Yu, H., Vardhanabhuti, V*., Wang, G*., 2022a. Stabilizing deep tomographic reconstruction: Part B. Convergence analysis and adversarial attacks. Patterns (N Y) 3, 100475. https://doi.org/10.1016/j.patter.2022.100475
• Wu, W., Hu, D., Cong, W., Shan, H., Wang, S., Niu, C., Yan, P., Yu, H., Vardhanabhuti, V*., Wang, G*., 2022b. Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results. Patterns (N Y) 3, 100474. https://doi.org/10.1016/j.patter.2022.100474
• Xie, C., Hu, Y., Han, L., Fu, J., Vardhanabhuti, V., Yang, H., 2022. Prediction of Individual Lymph Node Metastatic Status in Esophageal Squamous Cell Carcinoma Using Routine Computed Tomography Imaging: Comparison of Size-Based  Measurements and Radiomics-Based Models. Ann Surg Oncol. https://doi.org/10.1245/s10434-022-12207-7
• Xie C, Vardhanabhuti V*. PET/CT: Nasopharyngeal Cancers. PET clinics. (2022), S1556-8598 (21) 00114-0.
• Tammemägi MC, … Vardhanabhuti V, Berg CD, Hung RJ, Janes SM, Fong K, Lam S. USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study. The Lancet Oncology (2022), 23, 138-148.
• Chan SK, …, Vardhanabhuti V, Kwong DLW, So TH, Ng CY, Leung TW, Luk MY, Lee AWM, Choi HCW, Pan JJ, Lee VHF. Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma. Oral Oncology (2022), 126, 105736.
• Lau KY-Y, Ng K-S, Kwok K-W, Tsia KK-M, Sin C-F, Lam C-W, Vardhanabhuti V*. An Unsupervised Machine Learning Clustering and Prediction of Differential Clinical Phenotypes of COVID-19 Patients Based on Blood Tests-A Hong Kong Population Study. Front Med (Lausanne) (2021), 8:764934.
• Leung WY, Luk HM, Vardhanabhuti V, Gao Y, Hui KF, Lau WY, et al. Infantile to late adulthood onset facioscapulohumeral dystrophy type 1: a case series. Hong Kong medical journal (2021), 27(6):444–9.
• Cao P, Cui D, Ming Y, Vardhanabhuti V, Lee E, Hui E. Accelerating Magnetic Resonance Fingerprinting Using Hybrid Deep Learning and Iterative Reconstruction. Investigative Magnetic Resonance Imaging (2021), 25(4):293–9.
• Li YYS, Vardhanabhuti V, Tsougenis E, Lam WC, Shih KC. A Proposed Framework for Machine Learning-Aided Triage in Public Specialty Ophthalmology Clinics in Hong Kong. Ophthalmol Ther. (2021), 10(4):703–13. No citation. [IF 3.536, Rank 16/62. Q2 (Ophthalmology).]
• Wang M, Perucho JAU, Vardhanabhuti V, Ip P, Ngan HYS, Lee EYP. Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma. Acad Radiol. Sep 25;S1076-6332(21)00376-7 (2021). No citation. [IF 3.173, Rank 56/134. Q2 (Radiology, Nuclear Medicine & Medical Imaging).]
• W. Wu, D. Hu D, C. Niu, L.V Broeke, A.P.H Butler, P. Cao, J. Atlas, A. Chernoglazov, V. Vardhanabhut V*, G. Wang. Deep learning based spectral CT imaging. Neural Netw. Aug 28;144:342–58 (2021). Citations = 2. [Corresponding Author.] [IF 8.050, Rank 15/139, Q1 (Computer Science, Artificial Intelligence).]
• R. Du, E. D. Tsougenis, J. W. K. Ho, J. K. Y. Chan, K. W. H. Chiu, B. X. H. Fang, M. Y. Ng, S.-T. Leung, C. S. Y. Lo, H.-Y. F. Wong, H.-Y. S. Lam, L.-F. J. Chiu, T. Y. So, K. T. Wong, Y. C. I. Wong, K. Yu, Y.-C. Yeung, T. Chik, J. W. K. Pang, A. K.-C. Wai, M. D. Kuo, T. P. W. Lam, P.-L. Khong, N.-T. Cheung, V. Vardhanabhuti*, Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph. Sci. Rep. 11, 14250 (2021). Citations = 2. [Corresponding Author.] [IF 4.379, Rank 17/73, Q1 (Multidisciplinary Sciences).]
• A. W. L. Mui, A. W. M. Lee, V. H. F. Lee, W. T. Ng, V. Vardhanabhuti, S. S. Y. Man, D. T. T. Chua, S. C. K. Law, X. Y. Guan, Prognostic and therapeutic evaluation of nasopharyngeal carcinoma by dynamic contrast-enhanced (DCE), diffusion-weighted (DW) magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). Magn. Reson. Imaging. 83, 50–56 (2021). No citation. [IF 2.546, Rank 78/134. Q3 (Radiology, Nuclear Medicine & Medical Imaging).]
• W. Wu, P. Chen, S. Wang, V. Vardhanabhuti*, F. Liu, H. Yu, Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning. IEEE Trans Radiat Plasma Med Sci. 5, 537–547 (2021). Citations = 15. [Corresponding Author.]
• C.-Y. Xie, C.-L. Pang, B. Chan, E. Y.-Y. Wong, Q. Dou, V. Vardhanabhuti*, Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers . 13 (2021). No citation. doi:10.3390/cancers13102469. [Corresponding Author.]  [IF 6.639, Rank 51/242, Q1 (Oncology).]
• V. Vardhanabhuti*, K. S. Ng, Differential Impact of COVID-19 on Cancer Diagnostic Services Based on Body Regions: A Public Facility-Based Study in Hong Kong. Int. J. Radiat. Oncol. Biol. Phys. (2021), doi:10.1016/j.ijrobp.2021.05.010. No citation. [Corresponding Author.] [IF 7.038, Rank 11/134, Q1 (Radiology, Nuclear Medicine & Medical Imaging).]
• Wu W, Hu D, Niu C, Yu H*, Vardhanabhuti V*, Wang G*. DRONE: Dual-domain Residual-based Optimization NEtwork for Sparse-view CT Reconstruction. IEEE transactions on medical imaging. 2021. [Corresponding Author.]
• Xie CY, Hu YH, Ho JW, Han LJ, Yang H, Wen J, Lam KO, Wong IT, Law SY, Chiu KWH, Fu JH, Vardhanabhuti V*. Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study. Cancers. 2021;13(9). [Corresponding Author.]
• Dou Q, So TY, Jiang M, Liu Q, Vardhanabhuti V, Kaissis G, et al. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ digital medicine. 2021;4(1):60.
• Perucho JAU, Wang M, Vardhanabhuti V, Tse KY, Chan KKL, Lee EYP. Association between IVIM parameters and treatment response in locally advanced squamous cell cervical cancer treated by chemoradiotherapy. European Radiology. 2021.
• Wu W, Shi J, Yu H, Wu W, Vardhanabhuti V*. Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19. IEEE Transactions on Instrumentation and Measurement. 2021;70:1-12. [Corresponding Author.]
• So TY, Vardhanabhuti V*. Essential imaging of the nasopharyngeal space with special focus on nasopharyngeal carcinoma. Operative Techniques in Otolaryngology-Head and Neck Surgery. 2021 Feb 6. [Corresponding Author.]
• Wu W, Shi J, Yu H, Wu W, Vardhanabhuti V*. Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19. IEEE Transactions on Instrumentation and Measurement. 2021 Jan 19;70:1-2. [Corresponding Author.]
• Broeke LV, Grillon M, Yeung AW, Wu W, Tanaka R, Vardhanabhuti V*. Feasibility of photon-counting spectral CT in dental applications—a comparative qualitative analysis. BDJ open. 2021 Jan 27;7(1):1-8. [Corresponding Author.]
• Ding J, Cao P, Chang CHC, Gao Y, Chan S, Vardhanabhuti V*. Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI. Insights into Imaging, in press 2020. [Corresponding Author.]
• Chiu WHK, Vardhanabhuti V, Poplavskiy D, Yu PLH, Du R, Yap AYH, et al. Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs. Journal of thoracic imaging, in press 2020.
• Hu Y, Xie C, Yang H, Ho JWK, Wen J, Han L, Chiu K, Fu J*, Vardhanabhuti V*. Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma. JAMA network open. 2020;3(9):e2015927. [Co-Corresponding Author.]
• Hu Y, Xie C, Yang H, Ho JWK, Wen J, Han L, Lam, K. O., Wong, I. Y. H., Law, S. Y. K, Chiu, K. W. H, Vardhanabhuti V*, Fu J*. Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma. Radiotherapy and oncology. 2020;154:6-13. [Co-Corresponding Author.]

Book Publication/Chapter

• Book Chapter: ‘Ionizing Radiation in Medical Imaging and Efforts in Dose Optimization’. V Vardhanabhuti and C Roobottom in Ionizing Radiation Book 1. InTech Publishing – Feb 2012.

• ‘MCQs for the First FRCR Examination’. V Vardhanabhuti, J James, R Nensey, T Ninan, R Gray and V Shuen. Oxford University Press – August 2010.

• Yuan H, Vardhanabhuti V, Khong P-L. Imaging of Nasopharyngeal Carcinoma.  Nasopharyngeal Carcinoma: Academic Press; 2019. p. 155-78.

Invited Speaker

International:

• ‘Cardiac AI’. At: Society of Cardiovascular Computed Tomography Asia Pacific Symposium 2022 (05/03/2022). – Invited Speaker

• ‘Artificial intelligence and Radiomics in Head and Neck Cancer’. At: American Society of Neuroradiology Conference, 2021 (25/05/2021). – Invited Speaker
• ‘PSMA PET-CT imaging in Prostate Cancer – Pearls and Pitfalls’. At: Radiology Asia 2019, Singapore (26^th April 2019). – Invited Speaker
• ‘AI in Radiomics: Using Deep Learning & Neural Network in Streamlining Medical Imaging’. At: Radiology Asia 2019, Singapore (25^th April 2019). – Invited Speaker
• ‘Radiomics-Superficial Learning about Data Science and AI: What Radiologists Should Know’. At: Radiology Asia 2019, Singapore (25^th April 2019). – Invited Speaker
• ‘Medical imaging perspectives of radiomics/radiogenomics in the era of precision oncology’. At: Sanming Project of Medicine - The 2nd International Symposium on Specialist Education and Advances in Radiation Oncology, HKU-Shenzhen Hospital, Shenzhen, China (1^st December 2018). – Invited Speaker
• ‘Low dose CT using iterative reconstruction - Implications for liver imaging.’ STAR Symposium Xiamen, China (14th May 2015). – Keynote Speaker
• ‘Multi-modality imaging approach of hepatocellular carcinoma’ – Shenzhen Hong Kong Liver Disease Conference, Shenzhen, China (19th Dec 2014). – Keynote Speaker
• ‘Imaging of Acute Aortic Syndromes – Evidence for CT Protocols’ – British Society of Cardiovascular Imaging (BSCI) Spring Meeting, Glasgow, United Kingdom (24th April 2014). – Invited Speaker
• ‘Use of Low Dose CT scanning using model-based iterative reconstruction – Preliminary Experience’ – Radiation Protection in CT, British Institute of Radiology, London, UK (6th November 2012). – Invited Speaker
• ‘Use of novel model-based iterative reconstruction technique (MBIR) in ultra-low dose CT scanning in clinical practice - Preliminary experience in 30 patients’ – Royal College of Radiologists Annual Scientific Meeting, London, United Kingdom (11th Sept 2012). – Invited Speaker
• ‘Basics of Cardiac MR Imaging’ – South West Radiology Assembly (SWRA), Gloucester, United Kingdom (June 2010). – Invited Speaker

Local:

• ‘Quantitative imaging assessment using radiomics and deep learning for RT treatment response and prognostication’ At: 9^th Joint Scientific Meeting of Royal College of Radiologists & Hong Kong College of Radiology 2021(13/11/2021). – Invited Speaker

• ‘Imaging of Metal-based Contrast Agents in Spectral CT’. At: Croucher Advanced Study Institute 2020 Metals in Biology and Medicine Conference - From Molecular Image to Drug Resistance (11^th December 2020). – Invited Speaker
• ‘Multi-parametric MRI prostate in a nutshell for urologists’. At: 2^nd Hands-On Transperineal Prostate Biopsy Workshop, Hong Kong (18^th October 2019). – Invited Speaker
• ‘Radiomics in Head and Neck Cancers’. At: Tri-Society Head and Neck Oncology Meeting 2019, Hong Kong (30^th August 2019). – Invited Speaker
• ‘Radiomics Applications in NPC’. At: The 4th Hong Kong Radiographers and Radiation Therapists Conference cum The First Greater Bay Area Forum on Radiological Imaging and Technology, Hong Kong (1^st June 2019). – Keynote Speaker
• ‘Quantitative Radiomics and Machine Learning Approach in NPC’. At: Hong Kong College of Radiologists Annual Meeting, Hong Kong (18^th November 2018). – Keynote Speaker
• ‘MRI Radiomics For Pre-treatment Prognostication In Patients With NPC Treated With Intensity-modulated Radiation Therapy’ At: Mini-Symposium on MRI in Radiation Therapy, Hong Kong (11^th January 2018). – Invited Speaker

International Conference Presentations (n = 73). 
Selected Published Proceedings (most recent 20):

• J. Lian, Y. She, Y. Long, F. Huang, J. Deng, Q. Dou, C. Chen, V. Vardhanabhuti. Overall survival prediction for stage II and stage III non-small cell lung cancer patients using a graph-based deep learning algorithm. Accepted In European Congress of Radiology (ECR) Summer 2022. Abstract to be published in Insights Imaging (2022).
• F. Huang, J. Lian, K. S. NG, V. Vardhanabhuti. The use of a graph convolutional neural network model based on fundus photograph derived vascular biomarkers to predict coronary artery disease based on the CT CAD-RADS scores. In European Congress of Radiology (ECR) Overture 2022. Abstract published in Insights Imaging (2022).
• F. K. Yeung, W. Y. Ip, V. Vardhanabhuti. Virtual non-contrast image generation from pre-clinical photon-counting spectral CT - a phantom study to evaluate the algorithm performance. In European Congress of Radiology (ECR) Overture 2022. Abstract published in Insights Imaging (2022).
• A. D. Bhattarai, K. V. Tan, V. Vardhanabhuti. Photon counting spectral computed tomography for localization and quantitative assessment of adipose tissue and vasculature. In European Congress of Radiology (ECR) Overture 2022. Abstract published in Insights Imaging (2022).
• K. S. NG, C. F. Chu, F. Huang, J. Lian, V. Vardhanabhuti. A meshfree approach for liver and vessel segmentation. In European Congress of Radiology (ECR) Overture 2022. Abstract published in Insights Imaging (2022).
• S.-K. Chan, C. Lin, S. H. Huang, T. C. Chau, Q. Guo, B. O’Sullivan, K.-O. Lam, S. C. Chau, A. S. Y. Chan, C.-C. Tong, V. Vardhanabhuti, D. L. W. Kwong, T. H. So, S. C. Y. Ng, T. W. Leung, M.-Y. Luk, A. W. M. Lee, C.-W. Choi, J. Pan, V. H.-F. Lee. Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma. J. Clin. Oncol. 39, 6046–6046 (2021). Presented in ASCO 2021. 
• Ip W, Yeung DFK, Yung SPF, Vardhanabhuti V. Assessment of 3D printed phantom material for quantification accuracy, repeatability, and variation of material decomposition with pre-clinical photon-counting spectral CT. In Asian Oceanian Society of Radiology Conference, Malaysia, 2021. Abstract published in Korean Journal of Radiology 2021. 
• H Yu, V Vardhanabhuti, P Cao. Few-shot Meta-learning with Adversarial Shape Prior for Zonal Prostate Segmentation on T2 Weighted MRI. In International Society of Magnetic Resonance Imaging (ISMRM) 2021.
• F Huang, X Peng, V Vardhanabhuti, ESK Hui, HKF Mak, P Cao. “A semi-supervised level-set loss for white matter hyperintensities segmentation on FLAIR without manual label”. In International Society of Magnetic Resonance Imaging (ISMRM) 2021.
• Lee VHF, Chan ASY, Kwong DLW, Leung TW, Ng SCY, Tong CC, Lam KO, Vardhanabhuti V, Chan SK, and Choi CW. 2020. Phase II study of consolidative intensity-modulated radiation therapy following first-line palliative systemic chemotherapy for de novo previously untreated metastatic (M1) nasopharyngeal carcinoma. In ASCO, abstract published in J. Clin. Oncol. 2020
• Du R, Vardhanabhuti V. 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. In Medical Imaging Deep Learning (MIDL) 2020. Abstract published in Proceedings in Machine Learning Research, 2020. 
• C. Xie ,Y. Hu, L. Han, J. Fu, V. Vardhanabhuti, K. Chiu. Deep learning CT-based radiomics for prediction of treatment response to neoadjuvant chemoradiation in oesophageal squamous cell carcinoma. In European Congress of Radiology (ECR) 2020. Abstract published in Insights Imaging (2020).
• C. Xie, J. Ho, H. Pang, R. Du, K. Chiu, E. Lee, V. Vardhanabhuti. Machine learning re-sampling techniques in imbalanced datasets improve prognostication performance in a multicentre cohort of head and neck cancer patients using a PET-based radiomics model. In European Congress of Radiology (ECR) 2020. Abstract published in Insights Imaging (2020).
• C Xie, Y Chen, V Vardhanabhuti. Primary tumour and lymph node radiomics assessment in PET-CT in non-metastatic nasopharyngeal carcinoma patients. In European Congress of Radiology. Vienna, European Society of Radiology, 2020. Abstract published in Insights Imaging (2020)
• Vardhanabhuti V. Simulation and Clinical Multi-Reader Study using Clinically-Led Deep Learning Automated Radiograph Triaging System. In European Society of Radiology. Vienna, European Congress of Radiology, 2020. http://dx.doi.org/10.26044/ecr2020/C-05006. Abstract published in Insights Imaging (2020).
• Xie C, Wu W, Vanden Broeke L, Vardhanabhuti V. Spectral CT imaging: potential for cathartic-free colonography using dual-contrast agents. In Radiological Society of North America (RSNA), Chicago, USA 2020.
• V Vardhanabhuti, HT Au, J Ding, EYP Lee, P Cao, E Hui. Repeatability of Magnetic Resonance Fingerprinting using ISMRM/NIST MRI Phantom in Philips 3T MRI Scanner. In International Society of Magnetic Resonance Imaging (ISMRM) 2020. 
• F Huang, V Vardhanabhuti, PL Khong, M-Y Ng, P Cao. A level-set reformulated as deep recurrent network for left/right ventricle segmentation on cardiac cine MRI. In International Society of Magnetic Resonance Imaging (ISMRM) 2020.
• J Ding, V Vardhanabhuti, E Lai, Y Gao, S Chan, P Cao. Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI. In International Society of Magnetic Resonance Imaging (ISMRM) 2020.
• Cao P, Cui D, Vardhanabhuti V, Hui SK. Optimization for Deep Learning Magnetic Resonance Fingerprinting for in vivo Brain and Abdominal MRI. In International Society of Magnetic Resonance Imaging (ISMRM) 2020.

Current Lab Members

• Dr Fan Huang – Post-doctoral Fellow
• Dr Kei Shing Douglas Ng – Post-doctoral Fellow
• Dr Abhisek Bhattarai – Post-doctoral Fellow
• Ms Jie Lian –PhD candidate (Presidential Scholarship)
• Mr Morteza Homayounfar – Research Assistant 

Previous Lab Members

• Jie Ding – former post-doctoral fellow, Aug 2019-May 2020 (now at the Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA)

• Lieza Vanden Broeke – former post-doctoral fellow, Oct 2019-Sept 2020 (now at MARS Bioimaging Ltd, Christchurch, New Zealand)
• Weiwen Wu – former post-doctoral fellow, Sept 2019 – Dec 2020 (now at the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, N.Y. USA)
• Richard Du – Graduated PhD 
• Chenyi Xie – Graduated PhD
• Chenyi Xie – Graduated PhD
• Miaoru Zhang - Previous Research Assistant
• Vivian Ip – Previous Research Assistant
• Felix Yung – Previous Research Assistant