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Authors
# Name
1 Mariana Aya Suzuki Uchida(mariaya25@usp.br)
2 Erikson Júlio de Aguiar(erjulioaguiar@usp.br)
3 Caetano Traina Jr.(caetano@icmc.usp.br)
4 Agma Traina(agma@icmc.usp.br)

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Reference
# Reference
1 Aktas, B., Ates, D. D., Duzyel, O., and Gumus, A. (2025). Diffusion-based data augmentation methodology for improved performance in ocular disease diagnosis using retinography images. International Journal of Machine Learning and Cybernetics, 16(5):3843–3864.
2 Azad, R., Aghdam, E. K., Rauland, A., Jia, Y., Avval, A. H., Bozorgpour, A., Karimijafarbigloo, S., Cohen, J. P., Adeli, E., and Merhof, D. (2024). Medical image segmentation review: The success of u-net. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):10076–10095.
3 Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A. A. (2020). Albumentations: Fast and flexible image augmentations. Information, 11(2).
4 Cavalcanti, A., Brandão, D., Bezerra, E., and Coutinho, R. (2024). Avaliação de técnicas de balanceamento de dados na detecções de fraude em transações online de cartão de crédito. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 694–700, Porto Alegre, RS, Brasil. SBC.
5 Codella, N., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H., and Halpern, A. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic).
6 Consortium, M. (2024). Monai: Medical open network for ai.
7 Goceri, E. (2023). Medical image data augmentation: techniques, comparisons and interpretations. Artificial Intelligence Review, 56(11):12561–12605
8 Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
9 Jin, K., Huang, X., Zhou, J., Li, Y., Yan, Y., Sun, Y., Zhang, Q., Wang, Y., and Ye, J. (2022). Fives: A fundus image dataset for artificial intelligence based vessel segmentation. Scientific Data, 9(1):475.
10 Joshi, R. C., Kumar Sharma, A., and Kishore Dutta, M. (2024). Visiondeep-ai: Deep learning-based retinal blood vessels segmentation and multi-class classification framework for eye diagnosis. Biomedical Signal Processing and Control, 94:106273.
11 Kumar, T., Brennan, R., Mileo, A., and Bendechache, M. (2024). Image data augmentation approaches: A comprehensive survey and future directions. IEEE Access, 12:187536–187571.
12 Laheras, L. P., Rodrigues, P. S., Lopes, F. J. P., Palmeira, O. F. J., Falcão, A. X., Benato, B. C., and Giraldi, G. A. (2021). Aumento de dados utilizando firefly e level sets aplicado à segmentação de imagens médicas e biológicas. Revista Eletrônica de Iniciação Científica em Computação, 19(2).
13 Rayed, M. E., Islam, S. S., Niha, S. I., Jim, J. R., Kabir, M. M., and Mridha, M. (2024). Deep learning for medical image segmentation: State-of-the-art advancements and challenges. Informatics in Medicine Unlocked, 47:101504.
14 Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pages 234–241, Cham. Springer International Publishing.
15 Tschandl, P., Rosendahl, C., and Kittler, H. (2018). The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5(1):180161.