Classificação de Graus ISUP em Imagens Histopatológicas de Próstata utilizando EfficientNet e Aprendizado com Perda Ordinal Focal
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 das classes. O melhor modelo foi EfficientNet-B0 que alcançou kappa quadrático de 0,856 e acurácia de 66,9%. A abordagem preservou a integridade ordinal dos graus, tornando pouco frequentes os erros extremos e demonstrando sua viabilidade clínica.Referências
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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.
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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.
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.
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., and Nielsen, H. (2000). Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics, 16(5):412–424.
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.
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1):1–26.
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.
Publicado
19/07/2026
Como Citar
RODRIGUES, Woshington V. S.; BORGES, Armando L.; ARAÚJO, Jose D.; DINIZ, João O. B.; C. FILHO, Antonio O..
Classificação de Graus ISUP em Imagens Histopatológicas de Próstata utilizando EfficientNet e Aprendizado com Perda Ordinal Focal. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 53. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 902-907.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2026.22195.
