Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis
By Andy Kitchen, J. Seah. 2017
31 citations recorded by the source corpus.
Generative Adversarial Neural Networks (GANs) are applied to the synthetic generation of prostate lesion MRI images. GANs have been applied to a variety of natural images, is shown show that the same techniques can be used in the medical domain to create realistic looking synthetic lesion images. 16mm x 16mm patches are extracted from 330 MRI scans from the SPIE ProstateX Challenge 2016 and used to train a Deep Convolutional Generative Adversarial Neural Network (DCGAN) utilizing cutting edge techniques. Synthetic outputs are compared to real images and the implicit latent representations induced by the GAN are explored. Training techniques and successful neural network architectures are explained in detail.
Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine
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In Computer Vision and Pattern Recognition
- Arvid Lundervold
- Shadi Albarqouni
- Christoph Baur
- B. van Ginneken
- Anirban Mukhopadhyay
- Nassir Navab
- Arjan Kuijper
- Salome Kazeminia
In Pulmonary and Respiratory Medicine
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “[215] Kitchen A, Seah J. Deep generative adversarial neural networks for realistic prostate lesion MRI synthesis, arXiv:1708.00129 (2017).”
- GANs for Medical Image Analysis (2018, background): “of generated samples aect a machine learning model generally remains an open question. Table 2: Unconditional GANs for Medical Image Synthesis Method Architecture Modality Dataset Resolution PR Code Kitchen and Seah (2017) DCGAN MRI Prostate Lesions SPIE ProstateX Challenge 2016 (330 MRI Scans) 16 1”