ArticleBMC bioinformatics2020
Keras R-CNN: library for cell detection in biological images using deep neural networks.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- A Facile Method Based on Faster R-CNN for Cell Detection in Microfluidic Devices.Analytical chemistry · 2026Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Article
- Article
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- Rapid and accurate recognition of erythrocytic stage parasites ofFrontiers in microbiology · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Making the most of bioimaging data through interdisciplinary interactions.Journal of cell science · 2024Review
- Spatial Diffusion for Cell Layout Generation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Evaluation of a Deep Learning Based Approach to Computational Label Free Cell Viability Quantification.bioRxiv : the preprint server for biology · 2024Article
- Study of active food processing technology using computer vision and AI in coffee roasting.Food science and biotechnology · 2024Article
- Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.Frontiers in immunology · 2024Article
- Comparing Deep Learning Performance for Chronic Lymphocytic Leukaemia Cell Segmentation in Brightfield Microscopy Images.Bioinformatics and biology insights · 2024Article
- High-content imaging as a tool to quantify and characterize malaria parasites.Cell reports methods · 2023Article
- Article
- Topology-Guided Multi-Class Cell Context Generation for Digital Pathology.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- Improving the Detection of Epidemic Clones inMicroorganisms · 2023Article
- Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning.Nanoscale · 2022Article
- Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research.Frontiers in cellular and infection microbiology · 2022Review
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundA common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection methods can be useful for identifying individual cells as well as their phenotype in one step. State-of-the-art deep learning for object detection is poised to improve the accuracy and efficiency of biological image analysis.
resultsWe created Keras R-CNN to bring leading computational research to the everyday practice of bioimage analysts. Keras R-CNN implements deep learning object detection techniques using Keras and Tensorflow ( https://github.com/broadinstitute/keras-rcnn ). We demonstrate the command line tool's simplified Application Programming Interface on two important biological problems, nucleus detection and malaria stage classification, and show its potential for identifying and classifying a large number of cells. For malaria stage classification, we compare results with expert human annotators and find comparable performance.
conclusionsKeras R-CNN is a Python package that performs automated cell identification for both brightfield and fluorescence images and can process large image sets. Both the package and image datasets are freely available on GitHub and the Broad Bioimage Benchmark Collection.
Indexed as
Identifiers
What OpenQuestion holds
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.