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Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation

2021-01-16 · Khrystyna Faryna, Kevin Koschmieder, Marcella M. Paul, Thomas van den Heuvel, Anke van der Eerden, Rashindra Manniesing, Bram van Ginneken

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.

📄 PDF Abstract BibTeX arXiv:2101.06468

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Data AugmentationImage GenerationImage-to-Image TranslationTranslation

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Batch Normalization 설명 없음
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Residual Connection 설명 없음
Cycle Consistency Loss Cycle Consistency Loss is a type of loss used for generative adversarial networks that performs unpaired image-to-image translation. It was introduced with the…
Sigmoid Activation 설명 없음
Instance Normalization Instance Normalization (also known as contrast normalization) is a normalization layer where: $$ y_{tijk} = \frac{x_{tijk} - \mu_{ti}}{\sqrt{\sigma_{ti}^2 +…
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DiagnosticTask 2