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TRANSCEND - Artificial intelligence for neurological diseases

TRanslating AI Networks to Support Clinical Excellence in Neuro Diseases

Establishing bidirectional interface through an interdisciplinary collaboration

The Brain and Mind Centre's Computational Neuroscience team has embarked upon a project of neuroimmaging. With a focus on medical imaging and clinical outcomes in neurological disease, TRANSCEND (TRanslating AI Networks to Support Clinical Excellence in Neuro Diseases) is an interdisciplinary collaboration aiming at establishing a permanent bidirectional interface between AI infrastructure in the research and clinical settings to optimise models of sub-clinical disease progression in MS, the most common cause of neurological disability in young adults. The project involves scientific research and engineering work addressing the most crucial challenges in the field of medical imaging, including privacy-preserving enablement, domain adaptation, learning from labels with high uncertainties, data harmonisation and biomarker development.



The strategic partnership between USYD, SNAC and IMED is uniquely placed to develop & translate new AI technologies into improved health and disease monitoring. With a track record of successful research-health provider collaboration at scale, the team consists of internationally recognised experts across neurology, radiology, basic and applied neuroimaging, computer vision and artificial intelligence, clinical trials and healthcare delivery and governance.

The TRANSCEND core team is composed of a group of AI researchers who are experts in computer vision and medical imaging, software engineers and neuro-imaging specialists. The team’s interdisciplinary and strategic collaboration with industry leaders like NVIDIA, IMED and an extensive network of clinical partners give the team a unique position and strong advantage to translate state-of-the-art scientific findings into real-world clinical impacts.

The Team

Prof. Michael Barnett is Professor of Neurology at the Brain and Mind Centre (BMC), University of Sydney, consultant neurologist at Royal Prince Alfred Hospital Sydney, and Director of the MS Clinic and MS Clinical Trials Unit at the BMC. He trained in neurology at Royal Prince Alfred Hospital and received further subspecialty training at the National Hospital for Neurology and Neurosurgery in London. He subsequently completed a PhD in MS pathophysiology at the University of Sydney.  He has particular research interests in MS neuropathology and neuroimaging.  He co-founded the Sydney Neuroimaging Analysis Centre (SNAC) in 2012 to develop novel MRI biomarkers of MS disease progression and provide the first regulatory-compliant neuroimaging analysis / central MRI reading for MS clinical trials in the Southern hemisphere.  He is a leader in the Brain and Mind Centre’s Computational Neuroscience Team, which is spearheading the development and application of AI-derived algorithms and imaging biomarkers to further understanding of the mechanisms of neurological disease, improve diagnostic specificity and enhance treatment paradigms.  He is lead CI on the TRANSCEND project.  He is also Director of the MS Research Australia Brain Bank, a member of the Australian MS Clinical Trials Network Scientific Committee and Chairman of the PACTRIMS Scientific Program Committee.


Prof. Fernando Calamante is Professor in Faculty of Engineering and Information Technology at USYD; Director of Sydney Imaging, the biomedical imaging Core Research Facility at the University of Sydney; President of the International Society of Magnetic Resonance in Medicine 2021-2022 (ISMRM); and Node Director of the USYD/ANSTO National Imaging Facility node. He was previously (2014-2018) laboratory Head, Imaging Division, Florey Institute of Neuroscience and Mental Health. Prof. Calamante is internationally recognised for developing advanced MRI methods, with substantive contributions to perfusion, diffusion & super-resolution MRI that address major  imitations in the field. His MRtrix software, downloaded >10,000x, is used in a host of neuroscience & clinical applications; and a longstanding collaboration with Siemens has seen his advanced methods incorporated into clinical applications available on MRI scanners throughout the world. He has 138 peer-reviewed journal articles, book chapters and books with 12564 citations (1735 in 2019); and holds 3 patents in the field.


A/Prof. Wanli Ouyang is Senior Lecturer in the School of Electrical and information Engineering at USYD; and an IEEE ‘Senior Member’ (Area Chair, Pattern Recognition (ICPR), 2018 & 2020; Area Chair Industrial Electronics and Applications (ICIEA), 2019-2020; and others). He is an international authority on deep learning and its application to computer vision/pattern recognition; and image processing, with >10,600 cites (>7500 in deep learning; 3938 in 2019) and 80 peer reviewed publications in the field (including 17 as first author). His research has been disseminated through more than 25 invited national and international lectures; and has been diversely translated for use in Honda’s Advanced Driving Assistant System; web-based pill image recognition undergoing commercialisation; and products in use by Samsung & Alibaba. He has a remarkable 12 granted and 60 filed patents.


A/Prof. Weidong Cai is Associate Professor, Director of Multimedia Laboratory, and Associate Director of Biomedical & Multimedia Information Technology (BMIT) Research Group in the School of Computer Science, the Faculty of Engineering at the University of Sydney. He is the Team Leader of Multimodal Neuroimage Computing at BMC, with expertise in image processing/retrieval, medical computer vision and pattern recognition, bioinformatics, and computational neuroscience. Dr. Cai has published more than 280 peer-reviewed papers in leading international journals and proceedings of top international conferences; and is the associate editor of Computational Visual Media (Springer) and Brain Informatics (a Springer interdisciplinary journal); and guest editor of Machine Vision and Applications (Springer) and Neurocomputing (Elsevier). He is also the Springer-Nature Brain Informatics & Health Book Series Editor, and Co-Editor of “Medical Computer Vision: Algorithms for Big Data”. His research has been translated to software used globally in research and clinical domains: n3D bioimage analysis, open-source toolkits for 3D Slicer & single neuron reconstruction toolkits for BigNeuron.


Dr. Ryan Sullivan is Product Specialist in the Characterization and Research Technology Group at USYD. He leads the implementation of the USYD Imaging Data Service; and is lead CI on the Australian Imaging Service program, a national platform comprising 11 universities & the National Imaging Facilities, in partnership with the AU Research Data Commons. He co-developed the NCRIS 5-year AU Microscopy eResearch Roadmap; and is an invited expert on the Health Studies Australian National Data Asset (HeSANDA) program. His leadership in big data and eResearch has been recognised by 8 invited National lectures in 2019.


Dr. Tim Wang is Chief Operating Officer at SNAC; and Senior Lecturer and NRF/MSRA Fellow at USYD. Dr Wang holds a PhD in MS imaging biomarkers, and was awarded the 2017 Peter Bancroft prize for outstanding PhD thesis. His research has generated 28 journal papers and 527 citations (156 in 2019) since 2013. Dr Wang’s imaging research has been disseminated through 8 invited national/international lectures, and he will be a keynote speaker (on medical imaging AI) at GTC 2020 in San Jose (March 2020). His imaging research has been implemented in Phase 2 and collaborative academic trials for MS.


Dr. Andy Shieh is Chief Software Architect at SNAC. Dr Shieh holds a PhD in medical physics, and was formerly NHMRC and Cancer Institute ECR Fellow at USYD. His work on innovative Tumour Tracking technology is being adopted in 2 phase I trials to improve lung cancer radiotherapy outcomes; and he is the primary inventor on two Patent Cooperative Treaties. Dr Shieh joined SNAC in 2019; his team is responsible for developing/deploying imaging informatics & AI tech for clinical use.


Dr. Ronald Shnier is Professor of Radiology, Chief Medical Officer and Director of AI at I-MED, and Vice President (and previously President) of the Australian Diagnostic Imaging Association (ADIA). Dr Shnier has unparalleled experience in imaging healthcare delivery (over 30 years) and has served on numerous national/international MRI advisory boards, while maintaining active applied research in MRI. Dr Shnier co-authored the OMERACT scoring system used internationally in the assessment of Rheumatoid Arthritis; and he is currently participating in prostate cancer trials in which MRI plays a critical role.


Dr. Lynette Masters is Managing Radiologist of the I-MED radiology centre at the Brain and Mind Centre. Dr Masters is a dual Neurology-Radiology trained neuroradiologist who has worked as co-investigator (and co-author) on 5 USYD/SNAC investigator studies / publications since 2012; and is the principal radiologist for the BMC MS Clinical Trials Unit, directed by CI Barnett. 

Dr. Mariano Cabezas is a senior research fellow at the University of Sydney. Dr. Cabezas holds a PhD in medical image analysis awarded at the University of Girona, and received a European (ECTRIMS-MAGNIMS) and Spanish (Juan de la Cierva – Incorporacion) fellowship to continue his research focusing on multiple sclerosis lesion and activity segmentation, using deep learning techniques. Dr. Cabezas joined the University of Sydney in 2020 to lead neuroimaging research projects on neurodegenerative diseases such as multiple sclerosis, Alzheimer's disease and Parkinson's disease and focusing in structural, diffusion and functional MRI.


Dr. Lei Bai is a postdoctoral research fellow at the School of Electrical and Information Engineering, the University of Sydney, Australia. His research interests lie in Machine Learning, Spatial-temporal Learning, and their applications (e.g., Intelligent Transportation, IoT Analytics, and Healthcare). Lei has published a set of peer-reviewed papers on top AI conferences and journals such as NeurIPS, CVPR, IJCAI, KDD, ICCV, Ubicomp, TCSVT, and TITS. He has served as a program committee member or reviewer for IEEE TPAMI, NeurIPS, ICML, ICLR, CVPR, ICCV, AAAI, IJCAI, KDD, ECCV, IEEE TIP, IEEE TMM, ACM TOSN, and so on. Lei is also a recipient of the 2020 Google Ph.D. Fellowship, 2020 UNSW Engineering Excellence Award, and 2021 Dean’s Award for

Outstanding Ph.D.


Dr. Dongnan Liu is an AI researcher at the Brain and Mind Centre and Sydney Neuroimaging Analysis Centre. He is also a postdoctoral research associate at the school of computer science, University of Sydney. In 2021, he has obtained a PhD degree in computer science from the University of Sydney. His research interests include computer vision, biomedical image analysis, and deep learning. Currently, he mainly focuses on the interdisciplinary topics of biomedical image analysis and artificial intelligence, such as segmentation, label-efficient studies, and synthesis. He has produced several peer-reviewed publications, such as CVPR, IJCAI, IEEE TIP, IEEE TMI, IEEE TMM, etc. 


Dr. Rui Zeng is a research fellow in FMH Translational Research Collective at Faculty of Medicine and Health. He has a strong background in statistical analysis, machine learning and computer vision research. To this day, he has published more than 30 articles in prestigious conferences and journals. He has led or contributed to the projects in the areas of computer vision, natural language processing, statistical modelling, machine learning and medical imaging with major organisations including Australian Institute of Sport, Agilent Technologies Co., Ltd., General Electric, and Sydney Neuroimaging Analysis Centre. These findings have been translated into the productions and services that foster the effective use of machine learning and artificial intelligence to increase productivity and gain business insights. His current mission in medical artificial intelligence (AI) is to advance the design of medical imaging methods and healthcare systems that underpin how human professionals, patients, and AI-powered healthcare systems can work together in a trustworthy, efficient, and predictable manner.


Geng Zhan is an AI research engineer at Sydney Neuroimaging Analysis Centre and is also a PhD candidate at University of Sydney’s Brain and Mind Centre. With a background of Master of Philosophy in computer vision and deep learning, he is interested in translational medical research and developing deep learning methods to facilitate the precision analysis of imaging biomarkers in the clinical workflow and advance our understanding of disease mechanisms. His work includes quantitative analysis of longitudinal and cross-sectional brain volume change, imaging segmentation and registration, disease prognosis prediction and biomarker development of multiple sclerosis.


Dongang Wang is a computer vision specialist at the Brain and Mind Centre and Chief AI Engineer at Sydney Neuroimaging Analysis Centre. With a background in artificial intelligence and medical imaging analysis, he has devoted himself to the translational medical research and development applying novel deep learning techniques in the clinical workflow, including qualitative analysis of brain-related disease diagnosis, quantitative semantic segmentation, security of data and algorithms, and deep learning algorithms applied in multiple medical centres and with limited annotations.


Yuling Luo is an AI engineer and neuroimaging specialist at Sydney Neuroimaging Analysis Centre.  Yuling has a Master of Information Technology from University of Sydney. She currently works with AI researchers and developers to build a federated learning AI learning system that can recognise biomarkers linked to neurological disease progression while preserving patient privacy and data security.


Anand Rane is a Machine learning engineer at Sydney Neuroimaging Analysis Centre. Anand has done his Masters in Data Science specialising in Advanced Data Analytics and NLP, he is currently working with AI engineers and researchers for development and deployment of the AI models in a Federated Learning setting. He is proficient in various programming languages and deep learning frameworks alongside he has extensive experience in developing full stack web applications with Conversation AI bots integrated in them.

Dewan Arun Singh is a full stack software engineer at Sydney neuroimaging Analysis Centre. He has extensive experience in developing software for a variety of end users including businesses and research organizations. Having completed his Masters in Data Science specialising in artificial intelligence, he takes pride in developing robust, scalable software and applications which focus on securing user data and right to privacy. Proficient in various programming languages his specialisations include data structures and algorithms, parallel computing, predictive analysis and machine learning.


Binita Shrestha is a highly motivated full stack engineer at SNAC and completed a master's degree of Information technology, with specialization in "Software Design and Development".

With a strong foundation in software engineering and programming principles, she has acquired extensive experiences in development of software and web applications, that are highly reliable, secured and user-friendly. She possess skills in various programming languages, platforms and systems, and excels in every stage of software development life cycle, including design, coding, testing, debugging and maintaining. Having passion for developing innovative software solutions that promote efficiency of organizational success, she always looks forward to learning and excel new technologies.

Linda Ly is a Senior Clinical Trials Manager and Image Analysis Lead at the Sydney Neuroimaging Analysis Centre (SNAC). She has a PhD in Biotechnology (UNSW) and was an MSRA Research Fellow at the University of Sydney where she established a proteomic workflow for analysis of archival formalin-fixed MS tissue. She has more than 10 years experience in histopathology of MS lesions and 7 years in neuroimaging in MS and is a highly skilled imaging analyst in brain atrophy and lesion activity. She currently leads the clinical trials team, where she co-ordinates study set-up and manages the day-to-day operations for central MRI reading for multi-centre research studies and clinical trials. She also manages SNAC’s Quality Management System and has led the company in successfully attaining and maintaining its ISO13485 and ISO9001 certification.


Shilpa Shelar is a Neuroimaging Analyst at SNAC. Shilpa completed a master’s degree in Biotechnology. She has acquired extensive experience in clinical research following her Diploma in Clinical Research and data management. Shilpa joined SNAC in 2019 since then she is responsible for neuroimaging analysis across all phases of Multiple Sclerosis clinical trials. She loves being involved in technical quality checks for various AI models, product and CT triage systems proudly developed at SNAC to ensure the product quality is improved and within the specifications.


James Yu is a neuroimaging analyst part of the trials team at SNAC. James recently completed a Bachelor of Science majoring in neuroscience and pharmacology and is currently studying a Master of Clinical Trials Research. The trials team is responsible for analysing all incoming MRI scans in addition to checking and correcting models generated by the AI team.


Aria Nguyen is TRANSCEND’s technical project manager. Her role is focused on managing AI research and engineering and the product development of TRANSCEND’s federated learning platform for neuroimaging AI. Aria brings in extensive expertise as a data scientist and 10 years of experience of running AI projects across several industries, including management consulting, commercial banking, natural resources, automation and sport analytics.


Joy Wang is an operations manager at SNAC.  After completing her master degrees in Professional Accounting and Commerce (Finance) at University of Sydney, Joy expanded her path in various industries from multinational corporations to private SME, and gained extensive experience in business operations. Joy first joined SNAC coordinating HR, finance and daily operations, and has since extended her responsibilities to ISO internal auditing and project financial reporting.