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Synthesis of Medical Images Using GANs

: Middel, Laura; Palm, Christoph; Erdt, Marius


Greenspan, H.:
Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. Proceedings : First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11840)
ISBN: 978-3-030-32688-3 (Print)
ISBN: 978-3-030-32689-0 (Online)
International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) <1, 2019, Shenzhen>
International Workshop on Clinical Image-Based Procedures (CLIP) <8, 2019, Shenzhen>
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) <22, 2019, Shenzhen>
Conference Paper
Fraunhofer IGD ()
Fraunhofer Singapore ()
medical imaging; Lead Topic: Individual Health; Research Line: Computer vision (CV); image analysis; segmentation; Computer Aided Diagnosis; machine learning; registration

The success of artificial intelligence in medicine is based on the need for large amounts of high quality training data. Sharing of medical image data, however, is often restricted by laws such as doctor-patient confidentiality. Although there are publicly available medical datasets, their quality and quantity are often low. Moreover, datasets are often imbalanced and only represent a fraction of the images generated in hospitals or clinics and can thus usually only be used as training data for specific problems. The introduction of generative adversarial networks (GANs) provides a mean to generate artificial images by training two convolutional networks. This paper proposes a method which uses GANs trained on medical images in order to generate a large number of artificial images that could be used to train other artificial intelligence algorithms. This work is a first step towards alleviating data privacy concerns and being able to publicly share data that still contains a substantial amount of the information in the original private data. The method has been evaluated on several public datasets and quantitative and qualitative tests showing promising results.