ArticleScientific reports2022
Generation of microbial colonies dataset with deep learning style transfer.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep neural networks in medical microbiology for bacterial colonies classification.Scientific reports · 2026Article
- [Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- From microscopy to antimicrobial decisions: a clinically grounded roadmap for critical care infectious diseases.Frontiers in artificial intelligence · 2026Review
- Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images.PLoS computational biology · 2025Article
- Article
- BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- Bacterial colony size growth estimation by deep learning.BMC microbiology · 2023Article
- Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier.PloS one · 2023Article
- Self-normalized density map (SNDM) for counting microbiological objects.Scientific reports · 2022Article
- Video frame prediction of microbial growth with a recurrent neural network.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully supervised fashion. The developed generator employs traditional computer vision algorithms together with a neural style transfer method for data augmentation. We show that the method is able to synthesize a dataset of realistic looking images that can be used to train a neural network model capable of localising, segmenting, and classifying five different microbial species. Our method requires significantly fewer resources to obtain a useful dataset than collecting and labeling a whole large set of real images with annotations. We show that starting with only 100 real images, we can generate data to train a detector that achieves comparable results (detection mAP [Formula: see text], and counting MAE [Formula: see text]) to the same detector but trained on a real, several dozen times bigger dataset (mAP [Formula: see text], MAE [Formula: see text]), containing over 7 k images. We prove the usefulness of the method in microbe detection and segmentation, but we expect that it is general and flexible and can also be applicable in other domains of science and industry to detect various objects.
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