Aprendizado profundo aplicado ao reconhecimento de células em exames de Papanicolau
Resumo
Este artigo investiga o problema de apoio ao diagnóstico de câncer de colo de útero por meio da aplicação de aprendizado profundo para análise de células do exame Papanicolau. O trabalho apresenta um processo para classificação de células, além dos resultados de diferentes modelos convolucionais na tarefa de classificação.Referências
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Ji, J., Zhang, W., Dong, Y., Lin, R., Geng, Y., and Hong, L. (2023). Automated cervical cell segmentation using deep ensemble learning. BMC Medical Imaging, 23(1):137.
Mosiichuk, V., Sampaio, A., Viana, P., Oliveira, T., and Rosado, L. (2023). Improving mobile-based cervical cytology screening: A deep learning nucleus-based approach for lesion detection. Applied Sciences, 13(17).
Nayar, R. and Wilbur, D. (2015). The Bethesda System for Reporting Cervical Cytology. Definitions, Criteria, and Explanatory Notes.
Rezende, M. T., Silva, R., Bernardo, F. d. O., Tobias, A. H. G., Oliveira, P. H. C., Machado, T. M., Costa, C. S., Medeiros, F. N. S., Ushizima, D. M., Carneiro, C. M., and Bianchi, A. G. C. (2021). Cric searchable image database as a public platform for conventional pap smear cytology data. Scientific Data, 8(1):151.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR.
Zak, J., Grzeszczyk, M. K., Pater, A., Roszkowiak, L., Siemion, K., and Korzynska, A. (2022). Cell image augmentation for classification task using gans on pap smear dataset. Biocybernetics and Biomedical Engineering, 42(3):995–1011.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Ji, J., Zhang, W., Dong, Y., Lin, R., Geng, Y., and Hong, L. (2023). Automated cervical cell segmentation using deep ensemble learning. BMC Medical Imaging, 23(1):137.
Mosiichuk, V., Sampaio, A., Viana, P., Oliveira, T., and Rosado, L. (2023). Improving mobile-based cervical cytology screening: A deep learning nucleus-based approach for lesion detection. Applied Sciences, 13(17).
Nayar, R. and Wilbur, D. (2015). The Bethesda System for Reporting Cervical Cytology. Definitions, Criteria, and Explanatory Notes.
Rezende, M. T., Silva, R., Bernardo, F. d. O., Tobias, A. H. G., Oliveira, P. H. C., Machado, T. M., Costa, C. S., Medeiros, F. N. S., Ushizima, D. M., Carneiro, C. M., and Bianchi, A. G. C. (2021). Cric searchable image database as a public platform for conventional pap smear cytology data. Scientific Data, 8(1):151.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR.
Zak, J., Grzeszczyk, M. K., Pater, A., Roszkowiak, L., Siemion, K., and Korzynska, A. (2022). Cell image augmentation for classification task using gans on pap smear dataset. Biocybernetics and Biomedical Engineering, 42(3):995–1011.
Publicado
03/04/2024
Como Citar
SILVA, Henrique Castro e; GOMIDE, Leonardo Caetano; MACHADO, Alexei Manso Correa.
Aprendizado profundo aplicado ao reconhecimento de células em exames de Papanicolau. In: ESCOLA REGIONAL DE COMPUTAÇÃO APLICADA À SAÚDE (ERCAS), 9. , 2024, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 45-48.
DOI: https://doi.org/10.5753/ercas.2024.238704.
