Interpretability of ML models is a critical aspect when designing a system to support clinical decision-making.
Camila Roa
Camila Roa uses self-supervised learning to build representation spaces for retinal image datasets, and then visualises them in 2D using neighbor embedding algorithms. She is a PhD student and part of the IMPRS-IS graduate school. Her research interests include interpretability and robustness in medical applications of machine learning.
Interpretability of ML models is a critical aspect when designing a system to support clinical decision-making.