ArticleNature communications2012
Active learning framework with iterative clustering for bioimage classification.
Article in Nature communications, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 60 citations in OpenAlex.
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- Machine learning approach for discrimination of genotypes based on bright-field cellular images.NPJ systems biology and applications · 2021Article
- Thiazoline-related innate fear stimuli orchestrate hypothermia and anti-hypoxia via sensory TRPA1 activation.Nature communications · 2021Article
- Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.Cancer research · 2021Article
- Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI.Scientific reports · 2021Article
- Artificial hibernation/life-protective state induced by thiazoline-related innate fear odors.Communications biology · 2021Article
- A stomata classification and detection system in microscope images of maize cultivars.PloS one · 2021Article
- Machine learning identifies stroke features between species.Theranostics · 2021Article
- A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting.Plant phenomics (Washington, D.C.) · 2019Article
- IMACEL: A cloud-based bioimage analysis platform for morphological analysis and image classification.PloS one · 2019Article
- Stemness and anti-cancer drug resistance in ATP-binding cassette subfamily G member 2 highly expressed pancreatic cancer is induced in 3D culture conditions.Cancer science · 2018Article
- Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Towards enhanced and interpretable clustering/classification in integrative genomics.Nucleic acids research · 2017Article
- Interactive phenotyping of large-scale histology imaging data with HistomicsML.Scientific reports · 2017Article
- Trajectories of cell-cycle progression from fixed cell populations.Nature methods · 2015Article
- Semi-automatic organelle detection on transmission electron microscopic images.Scientific reports · 2015Article
- BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.BMC bioinformatics · 2013Article
- LIPS database with LIPService: a microscopic image database of intracellular structures in Arabidopsis guard cells.BMC plant biology · 2013Article
- An Interactive Learning Framework for Scalable Classification of Pathology Images.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big DataArticle
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in imaging systems have yielded a flood of images into the research field. A semi-automated facility can reduce the laborious task of classifying this large number of images. Here we report the development of a novel framework, CARTA (Clustering-Aided Rapid Training Agent), applicable to bioimage classification that facilitates annotation and selection of features. CARTA comprises an active learning algorithm combined with a genetic algorithm and self-organizing map. The framework provides an easy and interactive annotation method and accurate classification. The CARTA framework enables classification of subcellular localization, mitotic phases and discrimination of apoptosis in images of plant and human cells with an accuracy level greater than or equal to annotators. CARTA can be applied to classification of magnetic resonance imaging of cancer cells or multicolour time-course images after surgery. Furthermore, CARTA can support development of customized features for classification, high-throughput phenotyping and application of various classification schemes dependent on the user's purpose.
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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.