Metodologia de Geração de Dados aplicada à Geração de Imagens de Exames de Colonoscopia
Resumo
Os exames de colonoscopia podem detectar pólipos adenomatosos, que representam o estado inicial da grande maioria dos casos de câncer colorretal. Todavia, seus diferentes tamanhos e formatos tornam a tarefa de detectá-los difícil até mesmo para modelos estado da arte em aprendizado de máquina. Um dos motivos se deve à escassez de dados disponíveis para esta tarefa. Visando contornar esta problemática, é proposta neste artigo uma metodologia de geração de dados artificiais para imagens de colonoscopia, na qual se analisam três modelos generativos de imagens (Guided Diffusion, PFGM++ e o Improved Diffusion) e se realiza o tratamento e a avaliação das imagens geradas. Os resultados alcançados demonstram desempenho satisfatório na geração de imagens de exames de colonoscopia para todos os modelos generativos analisados, obtendo-se valores de FID igual a 33,89 e 0,2573 de SSIM para o melhor modelo generativo dentre todos os modelos avaliados e existentes na literatura.
Palavras-chave:
colonoscopia, pólipos, câncer colorretal, modelos generativos, geração de dados
Referências
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Pishva, A. K., Thambawita, V., Torresen, J., and Hicks, S. A. (2023). Repolyp: A framework for generating realistic colon polyps with corresponding segmentation masks using diffusion models. In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), pages 47–52.
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Thambawita, V., Salehi, P., Sheshkal, S. A., Hicks, S. A., Hammer, H. L., Parasa, S., Lange, T. d., Halvorsen, P., and Riegler, M. A. (2022). Singan-seg: Synthetic training data generation for medical image segmentation. PLOS ONE, 17(5):e0267976.
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Waisberg, E., Ong, J., Kamran, S. A., Masalkhi, M., Paladugu, P., Zaman, N., Lee, A. G., and Tavakkoli, A. (2024). Generative artificial intelligence in ophthalmology. Survey of Ophthalmology, Epub ahead of print. S0039-6257(24)00044-4.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612.
Wu, Z., Li, Y., Zhang, Y., Hu, H., Wu, T., Liu, S., Chen, W., Xie, S., and Lu, Z. (2020). Colorectal cancer screening methods and molecular markers for early detection. Technology in Cancer Research Treatment, 19:1533033820980426. Jan-Dec.
Xu, Y., Liu, Z., Tian, Y., Tong, S., Tegmark, M., and Jaakkola, T. (2023). Pfgm++: Unlocking the potential of physics-inspired generative models.
Zeng, Y., Fu, J., Chao, H., and Guo, B. (2021). Aggregated contextual transformations for high-resolution image inpainting.
Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., and Vilariño, F. (2015). Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics, 43:99–111. Epub 2015 Mar 20.
Dhariwal, P. and Nichol, A. (2021). Diffusion models beat gans on image synthesis.
Elhmadany, M., Elmadah, I., and Abdelmunim, H. (2024). Instance segmentation on distributed deep learning big data cluster. Journal of Big Data, 11.
Fagereng, J. A., Thambawita, V., Storås, A. M., Parasa, S., de Lange, T., Halvorsen, P., and Riegler, M. A. (2022). Polypconnect: Image inpainting for generating realistic gastrointestinal tract images with polyps.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., and Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a nash equilibrium. CoRR, abs/1706.08500.
Jha, D., Smedsrud, P. H., Riegler, M. A., Halvorsen, P., de Lange, T., Johansen, D., and Johansen, H. D. (2020). Kvasir-seg: A segmented polyp dataset. In International Conference on Multimedia Modeling, pages 451–462. Springer.
Lan, L., You, L., Zhang, Z., Fan, Z., Zhao, W., Zeng, N., Chen, Y., and Zhou, X. (2020). Generative adversarial networks and its applications in biomedical informatics. Frontiers in Public Health, 8:164.
Marques, A. F., Marques, K. F., Beraldo, M. N. M. d. S., Lima, T. B., Sassaki, L. Y., and Beraldo, R. F. (2023). Inteligência artificial na colonoscopia no rastreio do câncer colorretal: revisão de literatura. Brazilian Journal of Health Review, 6(4):18764–18774.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Inc., New York, NY, USA.
Nazeri, K., Ng, E., Joseph, T., Qureshi, F. Z., and Ebrahimi, M. (2019). Edgeconnect: Generative image inpainting with adversarial edge learning.
Nichol, A. and Dhariwal, P. (2021). Improved denoising diffusion probabilistic models.
Pishva, A. K., Thambawita, V., Torresen, J., and Hicks, S. A. (2023). Repolyp: A framework for generating realistic colon polyps with corresponding segmentation masks using diffusion models. In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), pages 47–52.
Siegel, R. L., Giaquinto, A. N., and Jemal, A. (2024). Cancer statistics, 2024. CA: A Cancer Journal for Clinicians, 74(1):12–49.
Erratum in: CA Cancer J Clin. 2024 Mar-Apr;74(2):203. DOI: 10.3322/caac.21830.
Silva, J., Histace, A., Romain, O., Dray, X., and Granado, B. (2014). Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. International Journal of Computer Assisted Radiology and Surgery, 9(2):283–293. Epub 2013 Sep 15.
Thambawita, V., Salehi, P., Sheshkal, S. A., Hicks, S. A., Hammer, H. L., Parasa, S., Lange, T. d., Halvorsen, P., and Riegler, M. A. (2022). Singan-seg: Synthetic training data generation for medical image segmentation. PLOS ONE, 17(5):e0267976.
Viscaino, M., Torres Bustos, J., Muñoz, P., Auat Cheein, C., and Cheein, F. A. (2021). Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions. World Journal of Gastroenterology, 27(38):6399–6414.
Waisberg, E., Ong, J., Kamran, S. A., Masalkhi, M., Paladugu, P., Zaman, N., Lee, A. G., and Tavakkoli, A. (2024). Generative artificial intelligence in ophthalmology. Survey of Ophthalmology, Epub ahead of print. S0039-6257(24)00044-4.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612.
Wu, Z., Li, Y., Zhang, Y., Hu, H., Wu, T., Liu, S., Chen, W., Xie, S., and Lu, Z. (2020). Colorectal cancer screening methods and molecular markers for early detection. Technology in Cancer Research Treatment, 19:1533033820980426. Jan-Dec.
Xu, Y., Liu, Z., Tian, Y., Tong, S., Tegmark, M., and Jaakkola, T. (2023). Pfgm++: Unlocking the potential of physics-inspired generative models.
Zeng, Y., Fu, J., Chao, H., and Guo, B. (2021). Aggregated contextual transformations for high-resolution image inpainting.
Publicado
05/12/2024
Como Citar
CASTRO, André Cerqueira; NEVES, Lucas Lima; GONÇALVES PAIVA, Heitor Sardinha; PEREIRA FRANCO, Ricardo Augusto.
Metodologia de Geração de Dados aplicada à Geração de Imagens de Exames de Colonoscopia. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 12. , 2024, Ceres/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 109-118.
DOI: https://doi.org/10.5753/erigo.2024.4797.
