ArticleNature biotechnology2018
Deep learning is combined with massive-scale citizen science to improve large-scale image classification.
Article in Nature biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Transfer Learning Approach with Features Block Selection via Genetic Algorithm for High-Imbalance and Multi-Label Classification of HPA Confocal Microscopy Images.Bioengineering (Basel, Switzerland) · 2025Article
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- ProteinFormer: protein subcellular localization based on bioimages and modified pre-trained transformer.BMC genomics · 2025Article
- Article
- Machine learning in healthcare citizen science: A scoping review.International journal of medical informatics · 2025Article
- Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE.Frontiers in robotics and AI · 2025Article
- Improving microbial phylogeny with citizen science within a mass-market video game.Nature biotechnology · 2025Article
- Deep 3D histology powered by tissue clearing, omics and AI.Nature methods · 2024Review
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.Bioinformatics (Oxford, England) · 2024Article
- PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data.Molecular systems biology · 2024Article
- GammaGateR: semi-automated marker gating for single-cell multiplexed imaging.bioRxiv : the preprint server for biology · 2023Article
- Online citizen science with the Zooniverse for analysis of biological volumetric data.Histochemistry and cell biology · 2023Article
- Unbiased single-cell morphology with self-supervised vision transformers.bioRxiv : the preprint server for biology · 2023Article
- A cell marker-based clustering strategy (cmCluster) for precise cell type identification of scRNA-seq data.Quantitative biology (Beijing, China) · 2023Article
- Single-cell subcellular protein localisation using novel ensembles of diverse deep architectures.Communications biology · 2023Article
- Self-Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Application of a deep learning image classifier for identification of Amazonian fishes.Ecology and evolution · 2023Article
- Practical recommendations from a multi-perspective needs and challenges assessment of citizen science games.PloS one · 2023Article
- Machine learning enhanced cell tracking.Frontiers in bioinformatics · 2023Article
9 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.