# Generation of microbial colonies dataset with deep learning style transfer

arXiv: https://arxiv.org/abs/2111.03789 · HTML: https://mlsanity.com/paper/2111.03789/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 70 references that are in the map.
- First posted on arXiv 6 Nov 2021.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Photorealistic Style Transfer via Wavelet Transforms](https://mlsanity.com/paper/1903.09760/) (2019)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Multi-style Generative Network for Real-time Transfer](https://mlsanity.com/paper/1703.06953/) (2017)
- [NiftyNet: a deep-learning platform for medical imaging](https://mlsanity.com/paper/1709.03485/) (2017)
- [DermGAN: Synthetic Generation of Clinical Skin Images with Pathology](https://mlsanity.com/paper/1911.08716/) (2019)
- [Skin Lesion Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/1902.03253/) (2019)
- [Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT](https://mlsanity.com/paper/1806.07777/) (2018)
- [Melanoma Detection using Adversarial Training and Deep Transfer Learning](https://mlsanity.com/paper/2004.06824/) (2020)
- [Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs](https://mlsanity.com/paper/2010.03975/) (2020)
- [Towards Adversarial Retinal Image Synthesis](https://mlsanity.com/paper/1701.08974/) (2017)
- [Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning](https://mlsanity.com/paper/2001.10695/) (2020)
- [AGAR a microbial colony dataset for deep learning detection](https://mlsanity.com/paper/2108.01234/) (2021)
- [Explainable Diabetic Retinopathy Detection and Retinal Image Generation](https://mlsanity.com/paper/2107.00296/) (2021)
- [High-Resolution Network for Photorealistic Style Transfer](https://mlsanity.com/paper/1904.11617/) (2019)
- [Deep Learning to Detect Bacterial Colonies for the Production of Vaccines](https://mlsanity.com/paper/2009.00926/) (2020)
- [A Real-World Demonstration of Machine Learning Generalizability: Intracranial Hemorrhage Detection on Head CT](https://mlsanity.com/paper/2102.04869/) (2021)
- [MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education](https://mlsanity.com/paper/2010.05177/) (2020)

## Built on by (most cited first, 2 of 2)

- [Self-Normalized Density Map (SNDM) for Counting Microbiological Objects](https://mlsanity.com/paper/2203.09474/) (2022)
- [Performing Video Frame Prediction of Microbial Growth with a Recurrent Neural Network](https://mlsanity.com/paper/2205.05810/) (2022)

## Abstract (arXiv)

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 = 0.416, and counting MAE = 4.49) to the same detector but trained on a real, several dozen times bigger dataset (mAP = 0.520, MAE = 4.31), containing over 7k 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.