Whilst current data-driven approaches have demonstrated the ability to democratise various fields in general, there are tremendous opportunities in healthcare that can leverage automated systems, from clinical decision-making in general medicine to surgical navigation. Advanced deep learning methods for computer vision are being widely adopted and further developed in the fields of medical and surgical image analysis, within both academia and industry. Despite the rapid development of these technologies, there remains a significant challenge in creating methods that are broad-ranging and generalisable across different populations and clinical environments.

In my presentation, I will take you through the journey of how I have engaged with recent advancements in machine learning to address some of these issues. Along with my students and a network of collaborators from clinical and computational backgrounds, we are working together to overcome these obstacles. I will also discuss my current work on incorporating data from multiple modalities and our initiatives to reduce biases. Another vital area of our focus has been on visualisation, particularly in understanding surgical scenes, where we have made progress from detailed mapping of internal structures like the oesophagus and colon to integrating augmented reality technologies in minimally invasive surgeries. I will also talk about the AI for surgical training of ESD project funded by the Worldwide Universities Network with CU HK as one of the partner universities.

Biography：

Sharib Ali, PhD, is a lecturer/assistant professor at the School of Computer Science, University of Leeds, UK, and a visiting fellow at the Department of Engineering Science, University of Oxford. He is one of the founding members of a non-profit research organisation in Nepal “NAAMII“, which is aimed at training students from LMIC countries and there he currently serves as a volunteering researcher position. He is an appointed member of the Royal Society’s Newton International Fellowships Committee and recipient of prestigious Springboard award from Academy of Medical Sciences and New Investigator Award from Engineering and Physical Sciences.

Previously, he was a postdoctoral researcher at the German Cancer Research Centre, Germany (2015-2018) and the University of Oxford, UK (2018-2022). He obtained his PhD in medical image analysis and understanding from the University of Lorraine, France, and his master’s degree in computer vision (by research) from the University of Bourgogne, France.

He has published over 30 peer-reviewed journals (including IEEE TMI, Medical Image Analysis, IEEE TNNLS, IEEE JBHI, NeuroImage, Pattern Recognition, CVIU, Gastroenterology, and AACR, etc.) and over 60 peer-reviewed conference works (including MICCAI, CVPR, IEEE ISBI and others). He is the initiator and lead of the endoscopic computer vision challenge series (EndoCV @ IEEE ISBI) and pre-operative to intraoperative challenge (P2ILF @ MICCAI2022), a joint effort between clinical and computational scientists, contributing towards large scale datasets in endoscopic and surgical image analysis tackling domain gaps and generalisability issues in medical imaging research. He has organised several workshop series at MICCAI, including Cancer Prevention through Early Detection (CaPTion) and Date Engineering in Medical Imaging (DEMI). He has also served as the lead organiser and general chair for the 29th Medical Image Understanding and Analysis conference 2025 and programme committee of the 37th IEEE CBMS conference in 2024. He serves as steering committee for both of these conferences now. Currently, he is leading a research group, “AI for Medicine and Surgery”, at University of Leeds, which focuses on developing computer vision methods for various medical, biomedical and surgical topics with translational impact. He has been invited to several groups as a speaker, including Vision Lab at Cambridge, HES-SO Valais, Switzerland; WEISS at UCL; and industry, including Roche and UCB. He was one of the invited speakers at the 22nd Mexican International Conference on Artificial Intelligence 2023, the London Biotechnology Show 2024 with 3000 attendees, and a keynote at the IEEE CBMS Conference 2024. He currently serves as guest lead editor for Frontiers in Medical technology for a research topic on Machine Learning for Medical Image Analysis and co-editor for special issue on Innovations in Artificial Intelligence for Medicine and Healthcare at Springer Health Information Science and Systems. I will also talk about the AI for surgical training of ESD project funded by the Worldwide Universities Network with CU HK as one of the partner universities.