ArticleNature methods2018
Quanti.us: a tool for rapid, flexible, crowd-based annotation of images.
Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Validation and tuning of in situ transcriptomics image processing workflows with crowdsourced annotations.PLoS computational biology · 2021Article
- A fully automated deep learning pipeline for high-throughput colony segmentation and classification.Biology open · 2020Article
- EVICAN-a balanced dataset for algorithm development in cell and nucleus segmentation.Bioinformatics (Oxford, England) · 2020Article
- ilastik: interactive machine learning for (bio)image analysis.Nature methods · 2019Review
- Deep learning for cellular image analysis.Nature methods · 2019Review
- Structured crowdsourcing enables convolutional segmentation of histology images.Bioinformatics (Oxford, England) · 2019Article
- Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Article
- Scientific Discovery Games for Biomedical Research.Annual review of biomedical data science · 2019Article
- Spatial proteomics: a powerful discovery tool for cell biology.Nature reviews. Molecular cell biology · 2019Review
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8 authors.
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Abstract
We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.
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