Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 23 Feb 2021.
Builds on
8 of 24 references.
References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
- 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
- 2018Data Augmentation using Random Image Cropping and Patching for Deep CNNscited by 87
- 2018StainGAN: Stain Style Transfer for Digital Histological Imagescited by 68
- 2019GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysiscited by 22
- 2018Iterative annotation to ease neural network training: Specialized machine learning in medical image analysiscited by 21
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Citing papers per year: 2021: 1.
1 paper.