Based on the HPA Cell Atlas image collection, a computational competition was arranged to identify deep-learning solutions for classification of subcellular protein patterns. Challenges included training on highly imbalanced classes and predicting multiple labels per image. More than 2,000 teams participated, and the winning models far outperformed our previous model. These models can be used as classifiers to annotate new images, feature extractors, or pretrained networks for a wide range of biological applications.

Key publication

• Ouyang W et al., Analysis of the Human Protein Atlas Image Classification competition. Nat Methods. (2019)
  PubMed: 31780840 DOI: 10.1038/s41592-019-0658-6