Cristin-resultat-ID: 1904192
Sist endret: 22. april 2021, 00:11
NVI-rapporteringsår: 2021
Resultat
Vitenskapelig artikkel
2021

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

Bidragsytere:
  • André Pedersen
  • Marit Valla
  • Anna Mary Bofin
  • Javier Perez de Frutos
  • Ingerid Reinertsen og
  • Erik Smistad

Tidsskrift

IEEE Access
ISSN 2169-3536
e-ISSN 2169-3536
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2021
Publisert online: 2021
Volum: 9
Sider: 1 - 14
Open Access

Importkilder

Scopus-ID: 2-s2.0-85104203814

Beskrivelse Beskrivelse

Tittel

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

Sammendrag

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

Bidragsytere

André Pedersen

  • Tilknyttet:
    Forfatter
    ved Institutt for klinisk og molekylær medisin ved Norges teknisk-naturvitenskapelige universitet
  • Tilknyttet:
    Forfatter
    ved Kirurgisk klinikk ved St. Olavs Hospital HF

Marit Valla

  • Tilknyttet:
    Forfatter
    ved Kirurgisk klinikk ved St. Olavs Hospital HF
  • Tilknyttet:
    Forfatter
    ved Institutt for klinisk og molekylær medisin ved Norges teknisk-naturvitenskapelige universitet
  • Tilknyttet:
    Forfatter
    ved Laboratoriemedisinsk klinikk ved St. Olavs Hospital HF

Anna Mary Bofin

  • Tilknyttet:
    Forfatter
    ved Institutt for klinisk og molekylær medisin ved Norges teknisk-naturvitenskapelige universitet

Javier Perez de Frutos

  • Tilknyttet:
    Forfatter
    ved Helse ved SINTEF AS

Ingerid Reinertsen

  • Tilknyttet:
    Forfatter
    ved Helse ved SINTEF AS
  • Tilknyttet:
    Forfatter
    ved Institutt for sirkulasjon og bildediagnostikk ved Norges teknisk-naturvitenskapelige universitet
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