Cargando…
CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images
We present a novel algorithm that is able to generate deep synthetic COVID-19 pneumonia CT scan slices using a very small sample of positive training images in tandem with a larger number of normal images. This generative algorithm produces images of sufficient accuracy to enable a DNN classifier to...
Autores principales: | Menon, Sumeet, Mangalagiri, Jayalakshmi, Galita, Josh, Morris, Michael, Saboury, Babak, Yesha, Yaacov, Yesha, Yelena, Nguyen, Phuong, Gangopadhyay, Aryya, Chapman, David |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10109233/ https://www.ncbi.nlm.nih.gov/pubmed/37069451 http://dx.doi.org/10.1007/s10278-023-00811-2 |
Ejemplares similares
-
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs
por: Mehta, Kushal, et al.
Publicado: (2021) -
CCS
por: Stickland, D P
Publicado: (2004) -
Conference Report: THIRD INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT (CIKM-94) Gaithersburg, MD November 29–December 1, 1994
por: Fong, Elizabeth, et al.
Publicado: (1995) -
Natural Language Processing for Breast Imaging: A Systematic Review
por: Diab, Kareem Mahmoud, et al.
Publicado: (2023) -
Prediction of Retention
Time and Collision Cross Section
(CCS(H+), CCS(H–), and CCS(Na+))
of Emerging Contaminants Using Multiple Adaptive Regression Splines
por: Celma, Alberto, et al.
Publicado: (2022)