Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation
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Abstract (arXiv)
We propose a novel framework for controllable pathological image synthesis for data augmentation. Inspired by CycleGAN, we perform cycle-consistent image-to-image translation between two domains: healthy and pathological. Guided by a semantic mask, an adversarially trained generator synthesizes pathology on a healthy image in the specified location. We demonstrate our approach on an institutional dataset of cerebral microbleeds in traumatic brain injury patients. We utilize synthetic images generated with our method for data augmentation in cerebral microbleeds detection. Enriching the training dataset with synthetic images exhibits the potential to increase detection performance for cerebral microbleeds in traumatic brain injury patients.
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References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017Improved Training of Wasserstein GANscited by 2,223
- 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
- 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
- 2018An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detectioncited by 30
- 2020Pseudo-healthy synthesis with pathology disentanglement and adversarial learningcited by 19
- 2019Generative Image Translation for Data Augmentation in Colorectal Histopathology Imagescited by 15
- 2019Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NETcited by 13
- 2019Generative Image Translation for Data Augmentation of Bone Lesion Pathologycited by 7
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