Histo-fetch - on-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training
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...
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| Main Authors: | , , , , |
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| Format: | Book |
| Published: |
Elsevier,
2022-01-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Summary: | 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. |
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| Item Description: | 2153-3539 10.4103/jpi.jpi_59_20 |