Work

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

Year: 2022

Type: article

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.

Source: Journal of Pathology Informatics

DOI: 10.4103/jpi.jpi_59_20

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

Institutions: University at Buffalo, State University of New York, Johns Hopkins University, Johns Hopkins Medicine

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,400

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Diabetes and Digestive and Kidney Diseases