Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation

• URL: http://arxiv.org/abs/2101.06468v1
• Date: Sat, 16 Jan 2021 15:58:17 GMT
• Title: Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation
• Authors: Khrystyna Faryna, Kevin Koschmieder, Marcella M. Paul, Thomas van den Heuvel, Anke van der Eerden, Rashindra Manniesing, Bram van Ginneken
• Abstract summary: 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. We demonstrate our approach on an institutional dataset of cerebral microbleeds in traumatic brain injury patients.
• Score: 5.674961386020127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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