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

TL;DR

FastPathology is an open-source platform that enables efficient, real-time deep learning analysis of large histopathological images on low-end hardware, integrating visualization, inference, and processing in one tool.

Contribution

It introduces a new C++ based platform that reduces memory usage and simplifies deployment of CNN models for digital pathology analysis.

Findings

FastPathology uses less memory than Java-based platforms.

Runtime varies with neural network model, hardware, and inference engine.

The platform supports real-time visualization and CNN inference on WSIs.

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, FastPathology, 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 and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.