Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training
We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology whole slide images (WSIs) for input to a neural network on-the-fly. We prefetch these patches as needed during network training, avoiding the need for WSI preparation such as chopping/tiling. We demonstrate the utility of this pipeline to perform artificial stain transfer and image generation using the popular networks CycleGAN and ProGAN, respectively.
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Methods
1x1 Convolution •
Batch Normalization •
Convolution •
Cycle Consistency Loss •
CycleGAN •
Dense Connections •
GAN Least Squares Loss •
Instance Normalization •
Leaky ReLU •
Local Response Normalization •
PatchGAN •
ProGAN •
ReLU •
Residual Block •
Residual Connection •
Sigmoid Activation •
Tanh Activation •
WGAN-GP Loss