Abstract
Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastPathology</i> , using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four 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 and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online on GitHub at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SINTEFMedtek/FAST-Pathology/</uri> .
Highlights
Whole Slide microscopy Images (WSIs) used in digital pathology are often large, and images captured at ×400 can have approximately 200k × 100k color pixels resulting in an uncompressed size of ∼ 56 GB [1]
We describe a novel application FastPathology based on FAST which consists of a Graphical User Interface (GUI) and open trained neural networks for analyzing digital pathology images
No significant difference was found between TensorFlow CUDA and TensorRT in any of the runtime experiments
Summary
Whole Slide microscopy Images (WSIs) used in digital pathology are often large, and images captured at ×400 can have approximately 200k × 100k color pixels resulting in an uncompressed size of ∼ 56 GB [1]. This exceeds the amount of Random-Access Memory (RAM) and Graphics Processing Unit (GPU) memory on most computer systems. CNNs are a class of artificial neural networks that can learn spatial features in the input data and are widely used in a range of computer vision tasks, including radiology and digital pathology. Still, deploying CNNs requires computer science expertise, making it difficult for clinicians
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