Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation
This work addresses data scarcity for medical imaging in traumatic brain injury, but it is incremental as it adapts existing CycleGAN methods to a specific domain.
The paper tackled the problem of limited data for cerebral microbleeds detection in traumatic brain injury patients by proposing a controllable image synthesis framework for data augmentation, resulting in potential increases in detection performance.
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.