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16 March 2020 Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis
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Abstract
Imbalanced training data introduce important challenge into medical image analysis where a majority of the data belongs to a normal class and only few samples belong to abnormal classes. We propose to mitigate the class imbalance problem by introducing two generative adversarial network (GAN) architectures for class minority oversampling. Here, we explore balancing data distribution 1) by generating new sample from unsupervised GAN or 2) synthesize missing image modalities from semi-supervised GAN. We evaluated the effect of the synthetic unsupervised and semi-supervised GAN methods by use of 1,500 MR images for brain disease diagnosis, where the classification performance of a residual network was compared between unbalanced datasets, classic data augmentation, and the proposed new GAN-based methods.The evaluation results showed that the synthesized minority samples generated by GAN improved classification accuracy up to 18% in term of Dice score.
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Mina Rezaei, Tomoki Uemura, Janne Näppi, Hiroyuki Yoshida, Christoph Lippert, and Christoph Meinel "Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140E (16 March 2020); https://doi.org/10.1117/12.2551166
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