Aplicando MultiInstance Learning (MIL) para o Diagnóstico de Câncer de Mama em Imagens Histopatológicas
Resumo
O câncer de mama é um dos cânceres mais comuns entre as mulheres pelo fato de este contabilizar 29,07% dos casos. Entre todas as variações da doença, o câncer de mama é o mais frequente no Brasil. E por esse motivo é imperativo que sejam desenvolvidas técnicas que agilizem o processo detecção destes tumores para diminuir a taxa de casos terminais. O aprendizado profundo tem se tornando um forte aliado dos patologistas na análise de imagens histopatológicas tomando decisão de maneira rápida e confiável. Neste trabalhos apresentamos uma abordagem baseada em MIL - Multi Instance Learning que tem uma abordagem diferente da tradicional devido ao fato de trabalha com varias instância de uma mesma imagem. Utilizamos para avaliar esse método o dataset de câncer de mama BreakHis. Nos experimentos realizados, foram alçados uma acurácia de 90% e 98% de sensibilidade para classificação binaria (Benigno ou Maligno).
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Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J. (2017). Deep sets. Advances in neural information processing systems, 30.
Cruz-Roa, A., Basavanhally, A., González, F., Gilmore, H., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., and Madabhushi, A. (2014). Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In Gurcan, M. N. and Madabhushi, A., editors, Medical Imaging 2014: Digital Pathology, volume 9041, pages 1 - 15. International Society for Optics and Photonics, SPIE.
da Silva, A., Júnior, I., and Amora, M. (2019). Breast cancer detection in histopathological images using convolutional neural networks. In Anais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 1-9, Porto Alegre, RS, Brasil. SBC.
Das, K., Conjeti, S., Chatterjee, J., and Sheet, D. (2020). Detection of breast cancer from whole slide histopathological images using deep multiple instance cnn. IEEE Access, 8:213502-213511.
Dauphin, Y. N., Fan, A., Auli, M., and Grangier, D. (2016). Language modeling with gated convolutional networks. CoRR, abs/1612.08083.
Dietterich, T. G., Lathrop, R. H., and Lozano-Pérez, T. (1997). Solving the multiple instance problem with axis-parallel rectangles. Artificial intelligence, 89(1-2):31-71.
Hering Jan, K. J. (2020). Multiple Instance Learning Via Deep Hierarchical Exploration for Histology Image Classification. International Symposium on Biomedical Imaging (ISBI), 17th edition.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708.
Ilse, M., Tomczak, J., and Welling, M. (2018). Attention-based deep multiple instance learning. In International conference on machine learning, pages 2127-2136. PMLR.
INCA (2021). Types of cancer - national cancer institute josé alencar gomes da silva inca. https://www.inca.gov.br/o-que-e-cancer.
Linkon, A. H. M., Labib, M. M., Hasan, T., Hossain, M., and Marium-E-Jannat (2021). Deep learning in prostate cancer diagnosis and gleason grading in histopathology images: An extensive study. Informatics in Medicine Unlocked, 24:100582.
Patil, A., Tamboli, D., Meena, S., Anand, D., and Sethi, A. (2019). Breast cancer histopathology image classification and localization using multiple instance learning. In 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pages 1-4.
Pereira, caroline Silva, F., Pereira, L. R. M., and Couto, S. D. (2021). Procedimentos para diagnóstico do câncer de mama: Revisão de literatura. Repositório Universitário da Anima (RUNA).
Rony, J., Belharbi, S., Dolz, J., Ayed, I. B., McCaffrey, L., and Granger, E. (2019). Deep weakly-supervised learning methods for classification and localization in histology images: a survey. CoRR, abs/1909.03354.
Santos, J., Veras, R., Silva, R., Aldeman, N., Aires, K., and Bianchi, A. (2019). Classificação de imagens de biópsias renais com glomeruloesclerose segmentar e focal ou com lesóƒes mínimas utilizando transfer learning em cnn. In Anais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 82-93, Porto Alegre, RS, Brasil. SBC.
Spanhol, F. A., Oliveira, L. S., Cavalin, P. R., Petitjean, C., and Heutte, L. (2017). Deep features for breast cancer histopathological image classification. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 1868-1873.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7):1455-1462.
Srinidhi, C. L., Ciga, O., and Martel, A. L. (2021). Deep neural network models for computational histopathology: A survey. Medical Image Analysis, 67:101813.
Sudharshan, P., Petitjean, C., Spanhol, F., Oliveira, L. E., Heutte, L., and Honeine, P. (2019). Multiple instance learning for histopathological breast cancer image classification. Expert Systems with Applications, 117:103-111.
Vogado, L., Veras, R., Araujo, F., Silva, R., and Aires, K. (2019). Rede neural convolucional para o diagnóstico de leucemia. In Anais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 46-57, Porto Alegre, RS, Brasil. SBC.
WHO (2020). Cancer today - world health organization.
Xu, G., Song, Z., Sun, Z., Ku, C., Yang, Z., Liu, C., Wang, S., Ma, J., and Xu, W. (2019). Camel: A weakly supervised learning framework for histopathology image segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10682-10691.
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J. (2017). Deep sets. Advances in neural information processing systems, 30.
Publicado
07/06/2022
Como Citar
FREITAS, Mario Pinto; LAUANDE, Marcos Gabriel Mendes; BRAZ JÚNIOR, Geraldo; OLIVEIRA, Marcus Vinicius; COSTA, Gabriel; LEVY, Matheus; PAIVA, Anselmo Cardoso de; ALMEIDA, João D. Sousa de.
Aplicando MultiInstance Learning (MIL) para o Diagnóstico de Câncer de Mama em Imagens Histopatológicas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 311-321.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222673.
