Papers › Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and...

Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training

23 Feb 2021arXiv:2102.11433archive 2025-07-28

Brendon Lutnick, Leema Krishna Murali, Brandon Ginley, Avi Z. Rosenberg, Pinaki Sarder

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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SarderLab/tf-WSI-dataset-utils officialmentioned in papermentioned on GitHubtf report

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Image Generationwhole slide images

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1x1 ConvolutionBatch NormalizationConvolutionCycle Consistency LossDense ConnectionsGAN Least Squares LossInstance NormalizationLocal Response NormalizationPatchGANProGANReLUResidual BlockResidual ConnectionSigmoid ActivationTanh ActivationWGAN-GP Loss

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