One of the biggest challenges in machine learning is to learn generalizable models from limited amounts of annotated data as creating annotated data is extremely costly and may limit novel findings. In this research project we study novel solutions to the challenge in the field of microscopy imaging of cancer cells using weakly-supervised and unsupervised learning. The developed methods and learned models will be applied in cancer cells and tissue studies to uncover unknown phenotypes and predictive biomarkers that may be clinically relevant for cancer patient survival. The outcome of the project will provide new knowledge in machine learning and enable solutions for various biological and medical questions regarding cancer function and treatment. The project will be done at the Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki.

2021

2026