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Methodology & Statistics Data Science Lab


31/05/2018: Crowdsourcing for Medical Image Analysis

Thank you again to Veronika and everyone who was present. Veronika’s PPT can now be downloaded here.

Original post:


Thursday 31/05/2018 at 15:00 in room B1.09

The next MSDSlab meetings will be this Thursday by Veronika Cheplygina (Eindhoven University of Technology) who will present on the possibilities of crowdsourcing of Medical Image Analysis in an interactive MSDSlab.


  • (Optional) If you want a bit to be a bit more familiar with terminology, take a look at Erickson et al – Machine Learning in Medical Imaging (https://pubs.rsna.org/doi/10.1148/rg.2017160130)
  • (Optional) Bring your favourite tools (R, Python etc) for analysing a small dataset and make sure you can load the data (group07.xlsx). This data, and information on it can be found on the MSDSlab GitHub page: https://github.com/msdslab/MSDSLAB31-05-2018

Machine learning (ML) has vast potential in medical image analysis, improving possibilities for early diagnosis and prognosis of disease. However, ML needs large amounts of representative, annotated examples for good performance, which may not always be possible with medical images. In this talk I will discuss how crowdsourcing is being used to address this problem. I will cover several existing approaches that do this, as well as discuss (what I think is) a promising alternative. At the end there will be an opportunity to play with some data to investigate this claim.

Veronika Cheplygina is an assistant professor at the Medical Image Analysis group, Eindhoven University of Technology since February 2017. She received her Ph.D. from the Delft University of Technology for her thesis “Dissimilarity-Based Multiple Instance Learning“ in 2015. As part of her PhD, she was a visiting researcher at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. From 2015 to 2016 she was a postdoc at the Biomedical Imaging Group Rotterdam, Erasmus Medical Center. Her research interests are centered around learning scenarios where few labels are available, such as multiple instance learning, transfer learning, and crowdsourcing. Next to research, Veronika blogs about academic life at http://www.veronikach.com


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