Why doesn’t it work in reality? – Bridging the gap between curated proof of concept tests and real world deployment of biomedical image based deep learning

Name: Ida-Maria Sintorn

Title of your project: Why doesn’t it work in reality?
– Bridging the gap between curated proof of concept tests and real world deployment of biomedical image based deep learning

Department: Department of Information Technology

Area of research: Computerised Image Processing

Summary of your project

Deep learning has lifted image processing and analysis to a whole new level, especially so for computer vision applications based on the enormous amounts of accessible images on internet. Biomedical applications follow but face a different scenario with less available training images, impact of erroneous results, trustworthiness and credibility. This project will focus on increasing the understanding of how to best incorporate application domain expertise to mitigate some shortcomings hindering the deployment of deep learning solutions in real-world clinical scenarios.  The aim of the project is to develop interactive verification and improvement approaches to increase the credibility and trustworthiness of image based deep learning in biomedical/clinical applications.  More specifically: 1) to explore strategies to identify rare or unexpected classes not encountered in the training set, and 2) to develop methods to interactively incorporate expert feedback regarding what features are important/false.

What do you look forward to the most during your sabbatical?

The multi- and interdisciplinary setting.