Abstract
Background: Training convolutional neural networks using pathology whole slide images (WSIs) is traditionally prefaced by the extraction of a training dataset of image patches. While effective, for large datasets of WSIs, this dataset preparation is inefficient. Methods: We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology 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. Results & Conclusions: We demonstrate the utility of this pipeline to perform artificial stain transfer and image generation using the popular networks CycleGAN and ProGAN, respectively. For a large WSI dataset, histo-fetch is 98.6% faster to start training and used 7535x less disk space.
Original language | English (US) |
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Pages (from-to) | 7 |
Number of pages | 1 |
Journal | Journal of Pathology Informatics |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2022 |
Keywords
- Convolutional neural network
- generative adversarial network
- tensorflow
- whole slide images
ASJC Scopus subject areas
- Pathology and Forensic Medicine
- Health Informatics
- Computer Science Applications