Machine Learning
Transforming data into knowledge
The Machine Learning team of DaMBi delves into both fundamental ML research as well as applications in bio-informatics.
In the realm of fundamental research, we focus mostly on explainability and adversarial robustness, striving to enhance the understanding of ML models and their shortcomings.
On the applied front, we tackle exciting challenges like cell segmentation, spatial transcriptomics, and imaging flow cytometry, leveraging ML techniques to unlock new insights and advancements in these domains.
Highlighted machine learning papers
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Classification of Human White Blood Cells Using Machine Learning for Stain-Free Imaging Flow Cytometry
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Maxim Lippeveld, Carly Knill, Emma Ladlow, Andrew Fuller, Louise J Michaelis, Yvan Saeys, Andrew Filby, Daniel Peralta
Cytometry Part A 2019