HASTE Team wins the Adipocyte Cell Imaging Challenge 2020 organised by AstraZeneca and AI Sweden

AI Sweden and AstraZeneca organised the Adipocyte Cell Imaging Challenge, a two-week-long hackathon to help AstraZeneca accelerate the drug development process. The task was to use machine learning in solving the problem of labelling cell images without requiring toxic preprocessing of cell cultures by predicting the content of the fluorescence images from the corresponding bright-field images.

Eight teams consisting of Swedish and international representatives from both academia and the private sector participated in the challenge. Our winning team consisted of Ankit Gupta, Håkan Wieslander, Phil Harrison, Ebba Bergman from HASTE Team and Erik Hallström from Wählby lab. The winner was selected by a jury consisting of representatives from AstraZenecaVinnova and AI Sweden.

To the left is the maximum projection of 7 brightfield images. In the middle, the corresponding superimposed fluorescence image of cell nuclei (blue), lipid droplets (green), and cytoplasm (red). To the right is the result of our proposed solution.

The solution used the Learning Under Privileged Information (LUPI) paradigm to solve the problem. LUPI enables the inclusion of additional (privileged) information when training machine learning models, data that is not available when making predictions. In this case, the segmentation masks of the nuclei were used as the privileged information during the training of machine learning models. Our solution will help AstraZeneca to speed up the drug discovery process and bring drugs to market quicker.

More information on challenge can be found here.

Press release about the solution can be found here, here, and here.