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Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis
6
Zitationen
22
Autoren
2023
Jahr
Abstract
rUMAP and nUMAP are great tools for image homogeneity analysis and bias discovery, as demonstrated by applying them to COVID-19 image data. Nonetheless, nUMAP could be applied to any type of data for which a deep neural network could be constructed. Advanced image super-resolution solutions are needed to reduce the impact of the resolution diversity on the classification network decision.
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Autoren
- Marek Socha
- Wojciech Prażuch
- Aleksandra Suwalska
- Paweł Foszner
- Joanna Tobiasz
- Jerzy Jaroszewicz
- Katarzyna Gruszczynska
- Magdalena Śliwińska
- Mateusz Nowak
- Barbara Giżycka
- Gabriela Zapolska
- Tadeusz Popiela
- Grzegorz Przybylski
- Piotr Fiedor
- Małgorzata Pawłowska
- Robert Flisiak
- Krzysztof Simon
- Jerzy Walecki
- Andrzej Cieszanowski
- Edyta Szurowska
- Michał Marczyk
- Joanna Polańska
Institutionen
- Silesian University of Technology(PL)
- Medical University of Silesia(PL)
- Jagiellonian University(PL)
- Medical University of Warsaw(PL)
- Collegium Medicum in Bydgoszcz(PL)
- Nicolaus Copernicus University(PL)
- Medical University of Białystok(PL)
- Wroclaw Medical University(PL)
- Central Clinical Hospital(PL)
- The Maria Sklodowska-Curie National Research Institute of Oncology(PL)
- Gdańsk Medical University(PL)