FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology

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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Andre Pedersen [verfasserIn] Marit Valla [verfasserIn] Anna M. Bofin [verfasserIn] Javier Perez De Frutos [verfasserIn] Ingerid Reinertsen [verfasserIn] Erik Smistad [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Deep learning neural networks high performance digital pathology decision support

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 58216-58229
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:58216-58229

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3072231

Katalog-ID:	DOAJ086612700

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callnumber-first	T - Technology
author	Andre Pedersen
spellingShingle	Andre Pedersen misc TK1-9971 misc Deep learning misc neural networks misc high performance misc digital pathology misc decision support misc Electrical engineering. Electronics. Nuclear engineering FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology
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topic	misc TK1-9971 misc Deep learning misc neural networks misc high performance misc digital pathology misc decision support misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Deep learning misc neural networks misc high performance misc digital pathology misc decision support misc Electrical engineering. Electronics. Nuclear engineering
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title	FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology
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title_sort	fastpathology: an open-source platform for deep learning-based research and decision support in digital pathology
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title_auth	FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology
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<FastPathology</italic<, 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<https://github.com/SINTEFMedtek/FAST-Pathology/</uri<.
abstractGer	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<FastPathology</italic<, 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<https://github.com/SINTEFMedtek/FAST-Pathology/</uri<.
abstract_unstemmed	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<FastPathology</italic<, 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<https://github.com/SINTEFMedtek/FAST-Pathology/</uri<.
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title_short	FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology
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FastPathology: An Open-Source Platform for Deep Learning-Based Research and Decision Support in Digital Pathology

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