AIDeveloper: Deep Learning Image Classification in Life Science and Beyond

Kraeter M, Abuhattum S, Soteriou D, Jacobi A, Krueger T, Guck J, Herbig M (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Advanced Science Wiley Open Access

Book Volume: 8

Article Number: 2003743

Journal Issue: 11

DOI: 10.1002/advs.202003743

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.

Authors with CRIS profile

Jochen Guck

Involved external institutions

Technische Universität Dresden DE Germany (DE) Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE)

How to cite

APA:

Kraeter, M., Abuhattum, S., Soteriou, D., Jacobi, A., Krueger, T., Guck, J., & Herbig, M. (2021). AIDeveloper: Deep Learning Image Classification in Life Science and Beyond. Advanced Science, 8(11). https://doi.org/10.1002/advs.202003743

MLA:

Kraeter, Martin, et al. "AIDeveloper: Deep Learning Image Classification in Life Science and Beyond." Advanced Science 8.11 (2021).

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