Classificação de texturas em imagens médicas através de modelos generativos e aprendizado autossupervisionado
Resumo
Este trabalho propõe um Variational Autoencoder regularizado para análise de textura, composto por um encoder, um decoder e um módulo preditor, com uma função de perda tripla que regulariza simultaneamente a codificação da imagem, sua reconstrução e classificação. Experimentos na análise de densidade mamográfica alcançaram níveis de precisão superiores aos de outros estudos publicados, sugerindo que o modelo pode fornecer uma representação latente mais separável e contribuir para a melhoria da análise de textura.Referências
Ha, R. et al. (2018) Convolutional neural network based breast cancer risk stratification using a mammographic dataset. Academic Radiology.
Kingma, D. P. and Welling, M. (2022). Auto-encoding variational bayes. arXiv.
Lehman, C. et al. (2019) Mammographic breast density assessment using deep learning: Clinical implementation. Radiology.
Liu, W. et al. (2020) Towards visually explaining variational autoencoders. In IEEE Computer Vision and Pattern Recognition.
Oliveira, J. . (2008). Toward a standard reference database for computer-aided mammography. In Medical Imaging: Computer-Aided Diagnosis.
Rahaman, N. (2019). On the spectral bias of neural networks. In International Conference on Machine Learning.
Rungue, A. H. A. (2019). Analysis of SVM parametrization in the classification of mammographic texture images. In Brazilian Congress on Automation.
Shehab, M. (2022). Machine learning in medical applications: A review of state-of-the-art methods. Computers in Biology and Medicine.
Song, Y. (2023). Latent traversals in generative models as potential flows. arXiv.
van der Maaten, L. and Hinton, G. (2008). Visualizing high-dimensional data using t-sne. Journal of Machine Learning Research.
Zeiser, F. et al. (2021) Deepbatch: A hybrid deep learning model for interpretable diagnosis of breast cancer in whole-slide images. Expert Systems with Applications.
Kingma, D. P. and Welling, M. (2022). Auto-encoding variational bayes. arXiv.
Lehman, C. et al. (2019) Mammographic breast density assessment using deep learning: Clinical implementation. Radiology.
Liu, W. et al. (2020) Towards visually explaining variational autoencoders. In IEEE Computer Vision and Pattern Recognition.
Oliveira, J. . (2008). Toward a standard reference database for computer-aided mammography. In Medical Imaging: Computer-Aided Diagnosis.
Rahaman, N. (2019). On the spectral bias of neural networks. In International Conference on Machine Learning.
Rungue, A. H. A. (2019). Analysis of SVM parametrization in the classification of mammographic texture images. In Brazilian Congress on Automation.
Shehab, M. (2022). Machine learning in medical applications: A review of state-of-the-art methods. Computers in Biology and Medicine.
Song, Y. (2023). Latent traversals in generative models as potential flows. arXiv.
van der Maaten, L. and Hinton, G. (2008). Visualizing high-dimensional data using t-sne. Journal of Machine Learning Research.
Zeiser, F. et al. (2021) Deepbatch: A hybrid deep learning model for interpretable diagnosis of breast cancer in whole-slide images. Expert Systems with Applications.
Publicado
09/06/2025
Como Citar
GOMIDE, Leonardo C.; MACHADO, Alexei M. C..
Classificação de texturas em imagens médicas através de modelos generativos e aprendizado autossupervisionado. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 967-972.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.6934.
