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AIDeveloper: deep learning image classification in life science and beyond

MPS-Authors
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Kräter,  Martin
Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;
Technische Universität Dresden;

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Abuhattum Hofemeier,  Shada
Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;
Technische Universität Dresden;

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Soteriou,  Despina
Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;

Jacobi,  Angela
Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;
Technische Universität Dresden;

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Guck,  Jochen
Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;
Technische Universität Dresden;
Max-Planck-Zentrum für Physik und Medizin, Max Planck Institute for the Science of Light, Max Planck Society;

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Herbig,  Maik
Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;
Technische Universität Dresden;

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AdvScience_2021_Krater_AIDeveloper.pdf
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Citation

Kräter, M., Abuhattum Hofemeier, S., Soteriou, D., Jacobi, A., Krüger, T., Guck, J., et al. (2021). AIDeveloper: deep learning image classification in life science and beyond. Advanced Science, 2003743. doi:10.1002/advs.202003743.


Cite as: https://hdl.handle.net/21.11116/0000-0007-D720-2
Abstract
Artificial intelligence (AI)‐based image analysis has increased drastically in recent years. However, all applications use individual solutions, highly specialized for a particular task. Here, an easy‐to‐use, adaptable, and open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming is presented. AID provides a variety of NN‐architectures, allowing to apply trained models on new data, obtain performance metrics, and export final models to different formats. AID is benchmarked on large image datasets (CIFAR‐10 and Fashion‐MNIST). Furthermore, models are trained to distinguish areas of differentiated stem cells in images of cell culture. A conventional blood cell count and a blood count obtained using an NN are compared, trained on >1.2 million images, and demonstrated how AID can be used for label‐free classification of B‐ and T‐cells. All models are generated by non‐programmers on generic computers, allowing for an interdisciplinary use.