AI Sweden and AstraZeneca have now picked out the teams that are going to participate in The Adipocyte cell imaging challenge. The challenge is to use machine learning to solve the problem of labeling cell images without requiring toxic preprocessing of cell cultures. The teams will kick off the challenge on Monday!
Applications have been open for the past three weeks and eight teams are now selected to participate in the Adipocyte Cell Imaging Challenge starting on Monday November 2nd.
The teams will utilize machine learning to combine the advantages of bright field and fluorescence imaging, while avoiding the toxic effects of cell labeling by predicting the content of the fluorescence images from the corresponding bright field images. This competition will help AstraZeneca to accelerate the drug development process.
Out of fifteen applications received, eight teams were finally selected to proceed.
The choice of participants was based on input from a jury with members both from AstraZeneca and AI Sweden. The applications were ranked using the combined scores of all members of the jury and the highest eight ranked proposals were chosen to be part of the challenge.
“We are really happy that all applications were of such high quality. Now, we are excited to see the results from the selected eight teams during the coming two weeks.” says Johanna Bergman, Head of Project Portfolio at AI Sweden.
The following teams are selected to participate in the challenge:
A team with researchers from Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, and Biological Research Centre of the Hungarian Academy of Sciences
An interdisciplinary team with extensive expertise in deep learning and biomedical image
analysis, including the analysis of large-scale phenotypic high-content imaging screens. All team members are senior researchers at Lund University, collaborating as part of the AI Lund network.
The HASTE Team consists of five PhD students from Uppsala University, Sweden. The team have a range of experience in image cytometry using both traditional and deep learning approaches
NordAxon Code Monkeys
The NordAxon Code Monkeys is a team of three employees from NordAxon, a regional Machine Learning consultancy business located in Malmö.
A team of two computer vision experts from Silo AI with a background related to healthcare and medical fields.
Soft Matter Lab @ GU
The Soft Matter Lab is a research group at the Department of Physics of the University of Gothenburg lead by Prof. Giovanni Volpe. The group focuses on research in biomedical optics, biomimetic active systems, neurosciences, and machine intelligence.
The Bug Hunters
A team of Medical Engineering graduates from KTH Royal Institute of Technology. The team has a background in medical imaging and machine learning, we are highly interested in data science for healthcare.
A multidisciplinary team consisting of three Deep Learning graduates from Linköping University and one 2nd year master’s student in Molecular Biology and Genetics at Izmir Institute of Technology.
On Monday November 2, the teams can access the data and can start to work on the challenge.
In two weeks on November 19, they will present their results at the challenge final that will take place online.
The final is open for anyone who is interested to participate and take part in the presentations.
The jury will evaluate the teams. A price check for 5000 dollars will be distributed.
Read more about the challenge here>>
Want to take part of the final presentations? Please contact email@example.com