Classificação de Imagens de Biópsias Renais com Glomeruloesclerose Segmentar e Focal ou com Lesões Mı́nimas Utilizando Transfer Learning em CNN
Resumo
Doenças renais crônicas surgem a partir de patologias agudas ou intermitentes não tratadas adequadamente como a doença de lesão mı́nima (DLM) e a glomeruloesclerose segmentar e focal (GESF). Identificar corre- tamente essas duas doenças é de suma importância, pois seus tratamentos e prognósticos são diferentes. Dessa forma, propomos um método capaz de dife- renciar DLM e GESF através de imagens de exames patológicos. No método proposto, foram extraı́das 10240 caracterı́sticas de três redes neurais convo- lucionais pré-treinadas, foram selecionadas 62 delas através do algoritmo de informação mútua e o Random Forest foi utilizado para a classificação. O método obteve acurácia de 93,33% e Kappa de 85,47%, o que é considerado “Quase Perfeito”.
Referências
Aha, D. W., Kibler, D., and Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1):37–66. http://dx.doi.org/10.1007/BF00153759
Barros, G. O., Navarro, B., Duarte, A., and Dos-Santos, W. L. (2017). Pathospotter-k: A computational tool for the automatic identification of glomerular lesions in histological images of kidneys. Scientific reports, 7:46769. http://dx.doi.org/10.1038/srep46769
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. arXiv preprint, pages 1610–02357.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37–46. http://dx.doi.org/10.1177/001316446002000104
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273–297. http://dx.doi.org/10.1007/BF00994018
Costa, D. M. d. N., Valente, L. M., Gouveia, P. A. d. C., Sarinho, F. W., Fernandes, G. V., Cavalcante, M. A. G. d. M., Oliveira, C. B. L. d., Vasconcelos, C. d. A. J. d., and Sarinho, E. S. C (2017). Comparative analysis of primary and secondary glomerulopathies in the northeast of brazil: data from the pernambuco registry of glomerulopathiesrepeg. Brazilian Journal of Nephrology, 39(1):29–35.
Ginley, B., Tomaszewski, J. E., and Sarder, P. (2017). Automatic computational labeling of glomerular textural boundaries. In Medical Imaging 2017: Digital Pathology, volume 10140, page 101400G. International Society for Optics and Photonics. http://dx.doi.org/10.1117/12.2254517
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition, volume 1, pages 278–282 vol.1. http://dx.doi.org/10.1109/ICDAR.1995.598994
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, pages 159–174. http://dx.doi.org/10.2307/2529310
Lopez, A. R., Giro-i Nieto, X., Burdick, J., and Marques, O. (2017). Skin lesion classification from dermoscopic images using deep learning techniques. In Biomedical Engineering (BioMed), 2017 13th IASTED International Conference on, pages 49–54. IEEE. http://dx.doi.org/10.2316/P.2017.852-053
Meng, D., Zhang, L., Cao, G., Cao, W., Zhang, G., and Hu, B. (2017). Liver fibrosis classification based on transfer learning and fcnet for ultrasound images. Ieee Access, 5:5804–5810. http://dx.doi.org/10.1109/ACCESS.2017.2689058
Moura, L. R., Franco, M. F., and Kirsztajn, G. M. (2015). Doença de lesões mínimas e glomeruloesclerose segmentar e focal em adultos: resposta a corticoide e risco de insuficiência renal. Jornal Brasileiro de Nefrologia, 37(4):475–480.
Pan, S. J., Yang, Q., et al. (2010). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345–1359.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252. http://dx.doi.org/10.1007/s11263-015-0816-y
Sarder, P., Ginley, B., and Tomaszewski, J. E. (2016). Automated renal histopathology: digital extraction and quantification of renal pathology. In Medical Imaging 2016: Digital Pathology, volume 9791, page 97910F. International Society for Optics and Photonics. http://dx.doi.org/10.1117/12.2217329
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312. http://dx.doi.org/10.1109/TMI.2016.2535302
Vogado, L. H., Veras, R. M., Araujo, F. H., Silva, R. R., and Aires, K. R. (2018). Leukemia diagnosis in blood slides using transfer learning in cnns and svm for classification. Engineering Applications of Artificial Intelligence, 72:415–422. http://dx.doi.org/10.1016/j.engappai.2018.04.024
Zhao, Y., Black, E. F., Marini, L., McHenry, K., Kenyon, N., Patil, R., Balla, A., and Bartholomew, A. (2016). Automatic glomerulus extraction in whole slide images towards computer aided diagnosis. In e-Science (e-Science), 2016 IEEE 12th International Conference on, pages 165–174. IEEE. http://dx.doi.org/10.1109/eScience.2016.7870897
Barros, G. O., Navarro, B., Duarte, A., and Dos-Santos, W. L. (2017). Pathospotter-k: A computational tool for the automatic identification of glomerular lesions in histological images of kidneys. Scientific reports, 7:46769. http://dx.doi.org/10.1038/srep46769
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. arXiv preprint, pages 1610–02357.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1):37–46. http://dx.doi.org/10.1177/001316446002000104
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273–297. http://dx.doi.org/10.1007/BF00994018
Costa, D. M. d. N., Valente, L. M., Gouveia, P. A. d. C., Sarinho, F. W., Fernandes, G. V., Cavalcante, M. A. G. d. M., Oliveira, C. B. L. d., Vasconcelos, C. d. A. J. d., and Sarinho, E. S. C (2017). Comparative analysis of primary and secondary glomerulopathies in the northeast of brazil: data from the pernambuco registry of glomerulopathiesrepeg. Brazilian Journal of Nephrology, 39(1):29–35.
Ginley, B., Tomaszewski, J. E., and Sarder, P. (2017). Automatic computational labeling of glomerular textural boundaries. In Medical Imaging 2017: Digital Pathology, volume 10140, page 101400G. International Society for Optics and Photonics. http://dx.doi.org/10.1117/12.2254517
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition, volume 1, pages 278–282 vol.1. http://dx.doi.org/10.1109/ICDAR.1995.598994
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, pages 159–174. http://dx.doi.org/10.2307/2529310
Lopez, A. R., Giro-i Nieto, X., Burdick, J., and Marques, O. (2017). Skin lesion classification from dermoscopic images using deep learning techniques. In Biomedical Engineering (BioMed), 2017 13th IASTED International Conference on, pages 49–54. IEEE. http://dx.doi.org/10.2316/P.2017.852-053
Meng, D., Zhang, L., Cao, G., Cao, W., Zhang, G., and Hu, B. (2017). Liver fibrosis classification based on transfer learning and fcnet for ultrasound images. Ieee Access, 5:5804–5810. http://dx.doi.org/10.1109/ACCESS.2017.2689058
Moura, L. R., Franco, M. F., and Kirsztajn, G. M. (2015). Doença de lesões mínimas e glomeruloesclerose segmentar e focal em adultos: resposta a corticoide e risco de insuficiência renal. Jornal Brasileiro de Nefrologia, 37(4):475–480.
Pan, S. J., Yang, Q., et al. (2010). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345–1359.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252. http://dx.doi.org/10.1007/s11263-015-0816-y
Sarder, P., Ginley, B., and Tomaszewski, J. E. (2016). Automated renal histopathology: digital extraction and quantification of renal pathology. In Medical Imaging 2016: Digital Pathology, volume 9791, page 97910F. International Society for Optics and Photonics. http://dx.doi.org/10.1117/12.2217329
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312. http://dx.doi.org/10.1109/TMI.2016.2535302
Vogado, L. H., Veras, R. M., Araujo, F. H., Silva, R. R., and Aires, K. R. (2018). Leukemia diagnosis in blood slides using transfer learning in cnns and svm for classification. Engineering Applications of Artificial Intelligence, 72:415–422. http://dx.doi.org/10.1016/j.engappai.2018.04.024
Zhao, Y., Black, E. F., Marini, L., McHenry, K., Kenyon, N., Patil, R., Balla, A., and Bartholomew, A. (2016). Automatic glomerulus extraction in whole slide images towards computer aided diagnosis. In e-Science (e-Science), 2016 IEEE 12th International Conference on, pages 165–174. IEEE. http://dx.doi.org/10.1109/eScience.2016.7870897
Publicado
11/06/2019
Como Citar
SANTOS, Justino Duarte; VERAS, Rodrigo de Melo Sousa; SILVA, Romuere Rodrigues Veloso e; ALDEMAN, Nayze Lucena Sangreman; AIRES, Kelson Romulo Teixeira; BIANCHI, Andrea Gomes Campos.
Classificação de Imagens de Biópsias Renais com Glomeruloesclerose Segmentar e Focal ou com Lesões Mı́nimas Utilizando Transfer Learning em CNN. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 82-93.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6244.
