AIDeveloper: Deep Learning Image Classification in Life Science and Beyond. Issue 11 (18th March 2021) - Kräter, Martin; Abuhattum, Shada; Soteriou, Despina; Jacobi, Angela; Krüger, Thomas; Guck, Jochen; Herbig, Maik
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. Abstract : In this work, AIDeveloper (AID), a software for training neural nets for image classification without need for programming is introduced. AID is open source and executables are provided for easy installation. The authors demonstrate AID by training models for classification of blood cells and natural images as well as detection of differentiated stem cells, reaching state of the art performance.