Pattern Recognition for Health

The focus of machine learning research on larger datasets, novelty and state-of-the-art results has lead to a lot of progress, but also has negative consequences such as propagating bias, a huge carbon footprint and de-democratization. We instead aim to recognize patterns within, and between problems with few examples, in particular related to the health domain. This includes:

  • understanding similarity and diversity of datasets
  • methods for learning with limited labeled data, such as transfer learning
  • meta-research on machine learning in medical imaging

People involved: Veronika Cheplygina, Bethany Chamberlain, Dovile Juodelyte, Ralf Raumanns (guest TU Eindhoven)