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.