Data Augmentation Techniques for Deep Learning-Based Medical Image Analyses
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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Presentation Transcript
Data Augmentation Techniques for Deep Learning-Based Medical Image Analyses Sun Woo Pi RTOS Lab Division of AI Computer Science & Engineering Kyonggi University
Introduction Deep Learning Data & Label( ) Overfitting( ) Dataset Dataset Dataset 2
Introduction Data Augmentation( ) Data Augmentation( ) Data Data Model Overfitting Data Augmentation( ) Generative Adversarial Network : GAN ( ) 3
Data Augmentation (ex : albumentaion) GAN Data Augmentation Data Augmentation Deep Learning Model 4
Data Augmentation Histogram Equalization (HE) Original 7
Data Augmentation Contrast Limited Adaptive Histogram Equalization (CLAHE) Original 8
Data Augmentation Blur 9
GAN Data Augmentation Generative Adversarial Network : GAN (unsupervised learning) Data 10
GAN Data Augmentation GAN Data Data Data Data 11
Data Augmentation Data Augmentation Cutout Mixup CutMix AugMix 12
Discussion Data Augmentation( ) Class & Data Data (overfitting) GAN GAN X Data GAN 13
Conclusion Data Augmentation Data GAN Data Model Data + GAN Data Data Model Data Augmentation Data Deep Learning Model 14
