FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology
| Autor: | Anna M. Bofin, Ingerid Reinertsen, Erik Smistad, André Pedersen, Javier Pérez de Frutos, Marit Valla |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
FOS: Computer and information sciences Computer Science - Machine Learning Source code decision support J.3 General Computer Science Computer science media_common.quotation_subject Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Convolutional neural network J.6 Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine FOS: Electrical engineering electronic engineering information engineering General Materials Science Inference engine Interactive visualization media_common Artificial neural network business.industry Deep learning I.5.4 Image and Video Processing (eess.IV) I.4.9 I.5.5 General Engineering Electrical Engineering and Systems Science - Image and Video Processing neural networks Visualization TK1-9971 030104 developmental biology Memory management Computer architecture 030220 oncology & carcinogenesis Artificial intelligence Electrical engineering. Electronics. Nuclear engineering business digital pathology high performance |
| Zdroj: | IEEE Access IEEE Access, Vol 9, Pp 58216-58229 (2021) 58216-58229 |
| Popis: | 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/. 12 pages, 4 figures, submitted to IEEE Access |
| Databáze: | OpenAIRE |
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