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  Indian J Med Microbiol
 

Figure 1: (a) The traditional method uses the CPU to chop whole slide images into patches which are saved to disk before convolutional neural network training. These patches are read and fed to the graphics processing unit for training. (b) Histo-fetch randomly selects indices containing tissue on the fly. These are processed on the CPU and supplied to the graphics processing unit. (c) Efficiency comparison of the two approaches using ProGAN, highlighting preprocessing time and additional disk space required using a dataset of 151 human biopsy whole slide images. The average training step time does not significantly change.

Figure 1: (a) The traditional method uses the CPU to chop whole slide images into patches which are saved to disk before convolutional neural network training. These patches are read and fed to the graphics processing unit for training. (b) Histo-fetch randomly selects indices containing tissue on the fly. These are processed on the CPU and supplied to the graphics processing unit. (c) Efficiency comparison of the two approaches using ProGAN, highlighting preprocessing time and additional disk space required using a dataset of 151 human biopsy whole slide images. The average training step time does not significantly change.