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Deep convolutional neural networks (CNNs) are the current state-of-the-artfor digital analysis of histopathological images. The large size of whole-slidemicroscopy images (WSIs) requires advanced memory handling to read, display andprocess these images. There are several open-source platforms for working withWSIs, but few support deployment of CNN models. These applications usethird-party solutions for inference, making them less user-friendly andunsuitable for high-performance image analysis. To make deployment of CNNsuser-friendly and feasible on low-end machines, we have developed a newplatform, FastPathology, using the FAST framework and C++. It minimizes memoryusage for reading and processing WSIs, deployment of CNN models, and real-timeinteractive visualization of results. Runtime experiments were conducted onfour different use cases, using different architectures, inference engines,hardware configurations and operating systems. Memory usage for reading,visualizing, zooming and panning a WSI were measured, using FastPathology andthree existing platforms. FastPathology performed similarly in terms of memoryto the other C++ based application, while using considerably less than the twoJava-based platforms. The choice of neural network model, inference engine,hardware and processors influenced runtime considerably. Thus, FastPathologyincludes all steps needed for efficient visualization and processing of WSIs ina single application, including inference of CNNs with real-time display of theresults. Source code, binary releases and test data can be found online onGitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.

FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology