Método Automático para classificação de Câncer de Próstata em WSIs utilizando EfficientNet, Ensemble e Modelagem de Perda Ordinal
Resumo
A avaliação histopatológica manual do câncer de próstata em WSIs está sujeita a variabilidades interobservador e intraobservador. Para mitigar esse problema, este trabalho propõe a classificação automática dos grupos de graus ISUP utilizando Redes Neurais Convolucionais. A metodologia utilizou o dataset PANDA , aplicando a redução de ruído por entropia e a extração de patches. Os modelos EfficientNet (B0, B3 e B7) foram treinados com a função Ordinal Focal Loss para preservar a progressão da doença e mitigar o desbalanceamento entre as classes. Por fim, as predições individuais foram combinadas em um ensemble. A estratégia de média simples obteve o melhor desempenho, com Kappa Quadrático Ponderado de 0,879 e acurácia de 69,8%. A abordagem preservou a integridade ordinal dos graus, reduzindo quase a zero os erros extremos e demonstrando sua viabilidade clínica.
Referências
Afifi, A., Alrahmawy, M., and El-Baz, A. (2024). Prostate cancer detection using deep learning models on histopathological image slides: An experimental analysis. International Journal of Advanced Computer Science and Applications, 15(1):xxx–xxx.
Albahri, O. S., Albahri, A. S., Mohammed, K. I., Zaidan, A., Zaidan, B. B., Hashim, M., Salman, O. H., Alaa, M., and Alsalem, M. A. (2022). Expert system research for prostate cancer: A literature review. Computers in Biology and Medicine, 145:105487.
Ali, I., Khan, A., Khan, M., Ahmad, R., and Ullah, I. (2023). Shannon entropy in artificial intelligence and its applications based on information theory. Entropy, 25(2):364.
Alici-Karaca, D. and Akay, B. (2024). An efficient deep learning model for prostate cancer diagnosis. IEEE Access, 12:150776–150790.
Araújo, F. H., Silva, R. R., Medeiros, F. N., Neto, J. F. R., Oliveira, P. H. C., Bianchi, A. G. C., and Ushizima, D. (2021). Active contours for overlapping cervical cell segmentation. International Journal of Biomedical Engineering and Technology, 35(1):70–92.
Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., and Jemal, A. (2024). Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3):229–263.
Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., and Litjens, G. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology, 21(2):233–241.
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Silva, V. W. K., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., and Fuchs, T. J. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine, 25(9):1301–1309.
Chen, X. (2026). A transfer learning-based deep focal multiclass network for psychological emotion recognition in community-correction populations. Alexandria Engineering Journal, 135:235–242.
Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4):213–220.
Dina, A. S., Siddique, A., and Manivannan, D. (2023). A deep learning approach for intrusion detection in internet of things using focal loss function. Internet of Things, 22:100699.
Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., and Humphrey, P. A. (2016). The 2014 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma: Definition of grading patterns and proposal for a new grading system. The American Journal of Surgical Pathology, 40(2):244–252.
Kosoko, I., Garg, A., Jain, S., and Hewage, P. (2024). Leveraging deep learning and explainable ai for diagnosis of prostate cancer. In Proceedings of the International Conference on Applied Artificial Intelligence in Medical Imaging, Bolton, United Kingdom.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 2980–2988.
PANDA Challenge (2020). Prostate cANcer grade assessment challenge. [link]. Accessed: 2024-01-01.
Siegel, R. L., Giaquinto, A. N., and Jemal, A. (2024). Cancer statistics, 2024. CA: A Cancer Journal for Clinicians, 74(1):12–49.
Xiang, J., Wang, X., Wang, X., Zhang, J., Yang, S., Yang, W., Han, X., and Liu, Y. (2023). Automatic diagnosis and grading of prostate cancer with weakly supervised learning on whole slide images. Computers in Biology and Medicine, 152:106340.
Zhou, Z.-H. (2025). Ensemble methods: foundations and algorithms. Chapman and Hall/CRC.
Albahri, O. S., Albahri, A. S., Mohammed, K. I., Zaidan, A., Zaidan, B. B., Hashim, M., Salman, O. H., Alaa, M., and Alsalem, M. A. (2022). Expert system research for prostate cancer: A literature review. Computers in Biology and Medicine, 145:105487.
Ali, I., Khan, A., Khan, M., Ahmad, R., and Ullah, I. (2023). Shannon entropy in artificial intelligence and its applications based on information theory. Entropy, 25(2):364.
Alici-Karaca, D. and Akay, B. (2024). An efficient deep learning model for prostate cancer diagnosis. IEEE Access, 12:150776–150790.
Araújo, F. H., Silva, R. R., Medeiros, F. N., Neto, J. F. R., Oliveira, P. H. C., Bianchi, A. G. C., and Ushizima, D. (2021). Active contours for overlapping cervical cell segmentation. International Journal of Biomedical Engineering and Technology, 35(1):70–92.
Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., and Jemal, A. (2024). Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3):229–263.
Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., and Litjens, G. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology, 21(2):233–241.
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Silva, V. W. K., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., and Fuchs, T. J. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine, 25(9):1301–1309.
Chen, X. (2026). A transfer learning-based deep focal multiclass network for psychological emotion recognition in community-correction populations. Alexandria Engineering Journal, 135:235–242.
Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4):213–220.
Dina, A. S., Siddique, A., and Manivannan, D. (2023). A deep learning approach for intrusion detection in internet of things using focal loss function. Internet of Things, 22:100699.
Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., and Humphrey, P. A. (2016). The 2014 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma: Definition of grading patterns and proposal for a new grading system. The American Journal of Surgical Pathology, 40(2):244–252.
Kosoko, I., Garg, A., Jain, S., and Hewage, P. (2024). Leveraging deep learning and explainable ai for diagnosis of prostate cancer. In Proceedings of the International Conference on Applied Artificial Intelligence in Medical Imaging, Bolton, United Kingdom.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 2980–2988.
PANDA Challenge (2020). Prostate cANcer grade assessment challenge. [link]. Accessed: 2024-01-01.
Siegel, R. L., Giaquinto, A. N., and Jemal, A. (2024). Cancer statistics, 2024. CA: A Cancer Journal for Clinicians, 74(1):12–49.
Xiang, J., Wang, X., Wang, X., Zhang, J., Yang, S., Yang, W., Han, X., and Liu, Y. (2023). Automatic diagnosis and grading of prostate cancer with weakly supervised learning on whole slide images. Computers in Biology and Medicine, 152:106340.
Zhou, Z.-H. (2025). Ensemble methods: foundations and algorithms. Chapman and Hall/CRC.
Publicado
01/06/2026
Como Citar
RODRIGUES, Woshington V. S.; C. FILHO, Antonio O.; BORGES, Armando L.; ARAÚJO, Jose D.; DINIZ, João O. B..
Método Automático para classificação de Câncer de Próstata em WSIs utilizando EfficientNet, Ensemble e Modelagem de Perda Ordinal. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 229-240.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2026.20665.
