Synthesis of Medical Images Using GANs

  • 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.

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Metadaten
Author:Luise Middel, Christoph PalmORCiDGND, Marius ErdtORCiD
DOI:https://doi.org/10.1007/978-3-030-32689-0_13
ISBN:978-3-030-32688-3
ISSN:0302-9743
Parent Title (English):Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019
Publisher:Springer Nature
Place of publication:Cham
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2019
Release Date:2020/05/04
Tag:Artificial Intelligence; Data privacy; Deep Learning; Generative adversarial networks; Machine Learning
GND Keyword:Neuronale Netze; Deep Learning; Bilderzeugung; Datenschutz
First Page:125
Last Page:134
Konferenzangabe:CLIP: Workshop on Clinical Image-Based Procedures UNSURE: International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
Institutes:Fakult├Ąt Informatik und Mathematik
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke
6 Technik, Medizin, angewandte Wissenschaften