Deep learning is combined with massive-scale citizen science to improve large-scale image classification

Sullivan DP, Winsnes CF, Åkesson L, Hjelmare M, Wiking M, Schutten R, Campbell L, Leifsson H, Rhodes S, Nordgren A, Smith K, Revaz B, Finnbogason B, Szantner A, Lundberg E

Nat Biotechnol 36 (9) 820-828 [2018-10-00; online 2018-10-01]

Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.

BioImage Informatics [Collaborative]

Bioinformatics (NBIS) [Collaborative]

Spatial Proteomics [Collaborative]

PubMed 30125267

DOI 10.1038/nbt.4225

Crossref 10.1038/nbt.4225

pii: nbt.4225