Data Augmentation Techniques for Deep Learning-Based Medical Image Analyses

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Various data augmentation techniques for improving deep learning-based medical image analyses. It covers topics such as overfitting, data labeling, and the use of generative adversarial networks (GANs).

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  1. Data Augmentation Techniques for Deep Learning-Based Medical Image Analyses Sun Woo Pi RTOS Lab Division of AI Computer Science & Engineering Kyonggi University

  2. Introduction Deep Learning Data & Label( ) Overfitting( ) Dataset Dataset Dataset 2

  3. Introduction Data Augmentation( ) Data Augmentation( ) Data Data Model Overfitting Data Augmentation( ) Generative Adversarial Network : GAN ( ) 3

  4. Data Augmentation (ex : albumentaion) GAN Data Augmentation Data Augmentation Deep Learning Model 4

  5. Data Augmentation 5

  6. Data Augmentation 6

  7. Data Augmentation Histogram Equalization (HE) Original 7

  8. Data Augmentation Contrast Limited Adaptive Histogram Equalization (CLAHE) Original 8

  9. Data Augmentation Blur 9

  10. GAN Data Augmentation Generative Adversarial Network : GAN (unsupervised learning) Data 10

  11. GAN Data Augmentation GAN Data Data Data Data 11

  12. Data Augmentation Data Augmentation Cutout Mixup CutMix AugMix 12

  13. Discussion Data Augmentation( ) Class & Data Data (overfitting) GAN GAN X Data GAN 13

  14. Conclusion Data Augmentation Data GAN Data Model Data + GAN Data Data Model Data Augmentation Data Deep Learning Model 14


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