Miguel has a PhD in Computer Science from the National Autonomous University of Mexico (UNAM). He worked as an assistant professor at the Faculty of Science at UNAM (2010-2017). In parallel, he spent several years working in the industry as a Business Analyst, SQA, and Project and Product Manager. He founded and directed a software startup until 2017, when he joined the Department of Computer Science and Software Engineering at the University of Canterbury (New Zealand) as a Lecturer.
He has been a member of major standardization bodies such as the Object Management Group and the ISO/IEC JTC 1/SC 7 Software and Systems Engineering subcommittee. He is also involved as a reviewer for international journals and conferences.
Miguel’s main research area is Software Quality, particularly software process improvement and product management. Miguel’s most recent research focused on Gamification in Computer Science and Software Engineering education. He is also interested in IT Project Management, Sustainability, and Databases.

This talk will delve into the challenges and opportunities I have identified while teaching Software Engineering as a subject (in Mexico) and now as a degree program (in New Zealand). From crafting innovative teaching methods to developing tools, I will illustrate how Software Engineering education is evolving to meet the industry’s ever-changing needs.

Leo Joskowicz 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 past President of the MICCAI Society, was the Secretary General of the International Society of Computer Aided Orthopaedic Surgery (CAOS International) and of 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 CAOS International and the 2007 Kaye Innovation Award. He has published two books and over 300 technical works including conference and journal papers, book chapters, and editorials and has 14 issued patents. He is on the Editorial Boards of Medical Image Analysis, Int. J. of Computer Aided Surgery, and Computer Aided Surgery and has served on numerous related program committees.

Monitoring cancer patients undergoing treatment requires the analysis by an expert clinician of radiological images acquired every few months. With improvements in treatments leading to longer patient lifespans and increased accessibility of scanners, longitudinal patient studies have increased in their number of CT and MRI scans, making their interpretation more challenging and time-consuming. While numerous AI and deep learning based methods have been developed for medical image analysis, they fail short of providing a viable solution for radiological follow-up.
In this talk, we present a novel fully automatic end-to-end pipeline for the comprehensive detection and segmentation of cancer lesions and the analysis of their evolution over time. The key novelties are: 1) SimU-Net, a simultaneous multi-channel 3D deep learning model trained on pairs of registered scans of each patient that identifies the lesions and their changes based on the lesion and healthy tissue appearance differences; 2) a model-based bipartite graph lesions matching method for the analysis of lesion changes at the lesion level; 3) a method for longitudinal analysis of consecutive scans of a patient based on SimU-Net. We demonstrate the method with experimental studies on liver and lung metastases in CT scans and brain metastases in MRI scans that establish state-of the-art results.