Challenging Sens.AI: Towards CE marked deep learning software for brain tumor analysis
We have witnessed the unprecedented success of deep learning in virtually all areas of science and industry, with medical image analysis not being an exception here. Although there are a plethora of deep learning-powered techniques that established the state of the art in the field, e.g., in the context of automatic delineation of human organs and tumors in various image modalities, deploying such methods in clinical settings is a challenging process. In this talk, we will discuss our approach for building Sens.AI – a CE marked deep learning-powered product for automated brain tumor analysis. We will show how to design a thorough evidence-based verification and validation of such techniques in scenarios, in which collecting large, heterogeneous, and high-quality ground-truth data is time-consuming, user-dependent and error prone.
Jakub Nalepa received his MSc (2011), PhD (2016), and DSc (2021) in Computer Science from Silesian University of Technology (SUT), Gliwice, Poland. Jakub is currently an Assistant Professor at SUT, Head of AI at KP Labs, Gliwice, Poland, and Machine Learning Architect at Future Processing Healthcare, Gliwice, Poland. His research interests focus on machine (deep) learning, signal processing, remote sensing, and medical image analysis. So far, he has published more than 100 peer-reviewed papers in journals and conferences and is an active reviewer in 90+ journals. Jakub has been awarded the Witold Lipski Prize (2017), and the POLITYKA Science Award (2020).