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