Generalizable deep neural networks for image quality classification of cervical images.

Ahmed SR, Befano B, Egemen D, Rodriguez AC, Desai KT, Jeronimo J, Ajenifuja KO, Clark C, Perkins R, Campos NG, Inturrisi F, Wentzensen N, Han P, Guillen D, Norman J, Goldstein AT, Madeleine MM, Donastorg Y, Schiffman M, de Sanjose S, Kalpathy-Cramer J, PAVE Study Group

Open source

DOI
10.1038/s41598-025-90024-0
Published
2025 Feb 21
Container
Scientific reports
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1038/s41598-025-90024-0,
  title = {Generalizable deep neural networks for image quality classification of cervical images.},
  author = {Ahmed SR and Befano B and Egemen D and Rodriguez AC and Desai KT and Jeronimo J and Ajenifuja KO and Clark C and Perkins R and Campos NG and Inturrisi F and Wentzensen N and Han P and Guillen D and Norman J and Goldstein AT and Madeleine MM and Donastorg Y and Schiffman M and de Sanjose S and Kalpathy-Cramer J and PAVE Study Group},
  year = {2025},
  journal = {Scientific reports},
  doi = {10.1038/s41598-025-90024-0},
  url = {https://doi.org/10.1038/s41598-025-90024-0}
}

RIS

TY  - JOUR
TI  - Generalizable deep neural networks for image quality classification of cervical images.
AU  - Ahmed SR
AU  - Befano B
AU  - Egemen D
AU  - Rodriguez AC
AU  - Desai KT
AU  - Jeronimo J
AU  - Ajenifuja KO
AU  - Clark C
AU  - Perkins R
AU  - Campos NG
AU  - Inturrisi F
AU  - Wentzensen N
AU  - Han P
AU  - Guillen D
AU  - Norman J
AU  - Goldstein AT
AU  - Madeleine MM
AU  - Donastorg Y
AU  - Schiffman M
AU  - de Sanjose S
AU  - Kalpathy-Cramer J
AU  - PAVE Study Group
PY  - 2025
JO  - Scientific reports
DO  - 10.1038/s41598-025-90024-0
UR  - https://doi.org/10.1038/s41598-025-90024-0
ER  - 

APA

SR, A., B, B., D, E., AC, R., KT, D., J, J., KO, A., C, C., R, P., NG, C., F, I., N, W., P, H., D, G., J, N., AT, G., MM, M., Y, D., M, S., S, D. S., J, K., & Group, P. S. (2025). Generalizable deep neural networks for image quality classification of cervical images.. Scientific reports. https://doi.org/10.1038/s41598-025-90024-0

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