FONDA

Foundations of Workflows for Large-Scale Scientific Data Analysis - A5: Workflows for Annotation-Efficient Machine Learning in Biomedical Imaging Research

01.01.2024 bis 01.01.2028

We aim to refine machine learning workflows in biomedicine by enhancing annotation efficiency. Currently, the lack of high-quality training data and time-consuming manual annotations hinder progress. To address this, we will explore self-supervised representation learning, which utilizes unlabeled data, potentially reducing the need for manual annotations. A5 will focus on creating efficient frameworks to train, explore, and benchmark such representation learning approaches.