The Hamlyn Symposium on Medical Robotics (HSMR) is now in its 13th year and has provided an annual forum for surgeons and engineers from across the globe to network and explore the latest developments in medical robotics. Every year researchers, clinicians and engineers are invited to submit papers on a range of topics covering clinical specialities in Urology, Cardiac Surgery, Neuro Surgery, Thoracic Surgery, General Surgery, Gynaecology, ENT, Orthopaedic and Paediatric Surgery.
This year we plan to build beyond the previous achievements and take the symposium to even higher successes with the theme of ‘Surgery and Beyond’. We have already received full CPD accreditation from the Royal College of Surgeons and to complement this we are planning a programme with increased focus on clinical practitioner centered talks, workshops and presentations.
Hamlyn Symposium on Medical Robotics 2021:
Featured Speaker Professor Leo Joskowicz
– Accelerating Deep Learning Segmentation And Modelling From Medical Images
We are pleased to announce Professor Leo Joskowicz, School of Computer Science and Engineering at the Hebrew University of Jerusalem, is one of the featured speakers of the Hamlyn Symposium on Medical Robotics 2021 (#HSMR21).
Abstract
Segmentation and geometric modeling of anatomical structures and pathologies from medical images is an essential component of many medical robotics systems. They are used in support of treatment selection, pre-operative planning, intraoperative execution and post-operative evaluation. In recent years, state-of-the-art methods for structures segmentation are based on deep learning classification algorithms that are starting to reach near human performance. However, developing deep learning methods requires large manually annotated datasets, which are seldom available and are expensive and time-consuming to create.
This talk will present an overview of our new methods for the fast development of deep learning-based image processing and segmentation solutions in with very few annotated datasets. The key idea is to bootstrap the creation of expert-validated annotations with new techniques for annotation uncertainty estimation and for learning how experts correct annotations generated by deep learning networks initially trained with very few annotated datasets. Our methods aim to optimize clinician time, reduce the annotated dataset size, and increase the accuracy and robustness of the deep neural networks results. We expect that our methods will significantly lower the entry cost, shorten the time and reduce the effort currently required to develop and deploy deep learning based solutions for Medical Robotics and Radiology.
Biography
Leo Joskowicz is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel since 1995.
He is the founder and director of the Computer-Aided Surgery and Medical Image Processing Laboratory (CASMIP Lab). Prof. Joskowicz is a Fellow of the IEEE, ASME, and MICCAI (Medical Image Processing and Computer Aided Intervention) Societies.
He is the President of the MICCAI Society and was the Secretary General of the International Society of Computer Aided Orthopaedic Surgery (CAOS) and the International Society for Computer Assisted Surgery (ISCAS). He is the recipient of the 2010 Maurice E. Muller Award for Excellence in Computer Assisted Surgery by the International Society of Computer Aided Orthopaedic Surgery and the 2007 Kaye Innovation Award.
He has published over 250 technical works including conference and journal papers, book chapters, and editorials and has 12 issued patents. He is on the Editorial Boards of six journals, including Medical Image Analysis, Int. J. of Computer Aided Surgery, Computer Aided Surgery, and Nature Scientific Reports and has served on numerous related program committees.