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access icon free Synthetic medical image generator for data augmentation and anonymisation based on generative adversarial network for glioblastoma tumors growth prediction

© The Institution of Engineering and Technology
Received 23/07/2020, Accepted 13/11/2020, Revised 16/10/2020, Published 07/12/2020

Prediction methods of glioblastoma tumours growth constitute a hard task due to the lack of medical data, which is mostly related to the patients' privacy, the cost of collecting a large medical data set, and the availability of related notations by experts.In this study, the authors propose a synthetic medical image generator (SMIG) with the purpose of generating synthetic data based on the generative adversarial network in order to provide anonymised data. In addition, to predict the glioblastoma multiform tumour growth the authors developed a tumour growth predictor based on end to end convolution neural network architecture that allows training on a public data set from the cancer imaging archive (TCIA), combined with the generated synthetic data. The authors also highlighted the impact of implicating a synthetic data generated using SMIG as a data augmentation tool. Despite small data size provided by TCIA data set, the obtained results demonstrate valuable tumour growth prediction accuracy.

Inspec keywords: data privacy; medical image processing; neural nets; cancer; tumours

Other keywords: generative adversarial network; end convolution neural network architecture; valuable tumour growth prediction accuracy; anonymisation; data augmentation tool; glioblastoma multiform tumour growth; glioblastoma tumors growth prediction; anonymised data; tumour growth predictor; TCIA data; medical data; cancer imaging archive; synthetic medical image generator; generated synthetic data; data size; glioblastoma tumours growth; prediction methods

Subjects: Neural computing techniques; Patient diagnostic methods and instrumentation; Other topics in statistics; Data security; Computer vision and image processing techniques; Optical, image and video signal processing; Biology and medical computing

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