ArticlePloS one2014
An active learning approach for rapid characterization of endothelial cells in human tumors.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 28 citations in OpenAlex.
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- CytoCensus, mapping cell identity and division in tissues and organs using machine learning.eLife · 2020Article
- A semi-supervised machine learning framework for microRNA classification.Human genomics · 2019Article
- ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning.Scientific reports · 2019Article
- Machine learning approaches for pathologic diagnosis.Virchows Archiv : an international journal of pathology · 2019Review
- An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms.Physiological measurement · 2019Article
- Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018Review
- 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
- A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome.Scientific reports · 2017Article
- Machine learning for epigenetics and future medical applications.Epigenetics · 2017Review
- Binge alcohol alters exercise-driven neuroplasticity.Neuroscience · 2017Article
- Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors.Bioinformatics (Oxford, England) · 2015Article
- Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure.Neuroinformatics · 2015Article
- 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
17 authors at 7 institutions in 1 country.
Funding
Abstract
Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.
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Registered trials
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.