Representações Esparsas Através de Dicionários para Processamento e Análise de Imagens Macroscópicas de Lesões Melanocíticas
Resumo
O melanoma é o tipo mais letal de câncer de pele, uma vez que é mais propenso à metástase. Especificamente, a taxa de pacientes que sobrevivem pelo menos cinco anos após o diagnóstico dessa doença no estágio inicial é superior a 99%. No entanto, essa taxa diminui para cerca de 25% se a detecção ocorre somente no último estágio. Nesse contexto, sistemas que auxiliem no diagnóstico precoce do melanoma podem desempenhar um papel de extrema importância, especialmente em regiões nas quais o acesso a dermatologistas é precário. Contudo, diferenciar um melanoma de lesões melanocíticas benignas pode ser uma tarefa desafiadora, mesmo para especialistas experientes. Para lidar com esse problema, nesta tese, propõe-se um sistema automático para detectação de melanoma a partir de uma simples fotografia digital, o qual baseia-se em modelos de representações esparsas. Os resultados apresentados pelo sistema proposto são promissores e sugerem que o sistema proposto pode potencialmente superar alternativas estado-da-arte e até mesmo médicos treinados.
Referências
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Brinker, T. J., Hekler, A., Hauschild, A., Berking, C., Schilling, B., Enk, A. H., Haferkamp, S., Karoglan, A., von Kalle, C., Weichenthal, M., et al. (2019). Comparing artificial intelligence algorithms to 157 german dermatologists: the melanoma classification benchmark. European Journal of Cancer, 111:30–37.
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., and Halpern, A. (2018). Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC). In Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), pages 168–172.
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Flores, E., Zortea, M., and Scharcanski, J. (2019). Dictionaries of deep features for land-use scene classification of very high spatial resolution images. Pattern Recognition, 89:32–44.
Giotis, I., Molders, N., Land, S., Biehl, M., Jonkman, M. F., and Petkov, N. (2015). MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Systems with Applications, 42(19):6578–6585.
Ha, Q., Liu, B., and Liu, F. (2020). Identifying melanoma images using efficientnet ensemble: Winning solution to the SIIM-ISIC melanoma classification challenge. Disponível em: https://arxiv.org/pdf/2010.05351.pdf. Último acesso em: 09 set. 2022.
Hasan, M. K., Dahal, L., Samarakoon, P. N., Tushar, F. I., and Martí, R. (2020). DSNet: Automatic dermoscopic skin lesion segmentation. Computers in Biology and Medicine, 120:103738.
Kawahara, J., Daneshvar, S., Argenziano, G., and Hamarneh, G. (2018). Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE Journal of Biomedical and Health Informatics, 23(2):538–546.
Ministério da Saúde (2019). Terapia-alvo (vemurafenibe, dabrafenibe, cobimetinibe, trametinibe) e imunoterapia (ipilimumabe, nivolumabe, pembrolizumabe) para o tratamento de primeira linha do melanoma avançado não-cirúrgico e metastático. CONITEC.
Pacheco, A. G., Trappenberg, T., and Krohling, R. A. (2020). Learning dynamic weights for an ensemble of deep models applied to medical imaging classification. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 1–8.
Qiu, Q., Patel, V. M., and Chellappa, R. (2014). Information-theoretic dictionary learning for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(11):2173–2184.
Scharcanski, J. and Celebi, M. E. (2014). Computer Vision Techniques for the Diagnosis of Skin Cancer. Springer.
Woza, M. (2018). Skin lesion analyzer. Disponível em: http://skin.test.woza.work/. Último acesso em: 09 set. 2022.
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., and Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2):210–227.
Zhang, J., Li, W., Ogunbona, P., and Xu, D. (2019). Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective. ACM Computing Surveys, 52(1):1–38.
Alcón, J. F., Ciuhu, C., ten Kate, W., Heinrich, A., Uzunbajakava, N., Krekels, G., Siem, D., and de Haan, G. (2009). Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE Journal of Selected Topics in Signal Processing, 3(1):14–25.
Amelard, R., Glaister, J., Wong, A., and Clausi, D. A. (2014). High-level intuitive features (HLIFs) for intuitive skin lesion description. IEEE Transactions on Biomedical Engineering, 62(3):820–831.
Bissoto, A., Fornaciali, M., Valle, E., and Avila, S. (2019). (de) constructing bias on skin lesion datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 2766–2774.
Brinker, T. J., Hekler, A., Hauschild, A., Berking, C., Schilling, B., Enk, A. H., Haferkamp, S., Karoglan, A., von Kalle, C., Weichenthal, M., et al. (2019). Comparing artificial intelligence algorithms to 157 german dermatologists: the melanoma classification benchmark. European Journal of Cancer, 111:30–37.
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., and Halpern, A. (2018). Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC). In Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), pages 168–172.
Flores, E. and Scharcanski, J. (2016). Segmentation of melanocytic skin lesions using feature learning and dictionaries. Expert Systems with Applications, 56:300–309.
Flores, E., Zortea, M., and Scharcanski, J. (2019). Dictionaries of deep features for land-use scene classification of very high spatial resolution images. Pattern Recognition, 89:32–44.
Giotis, I., Molders, N., Land, S., Biehl, M., Jonkman, M. F., and Petkov, N. (2015). MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Systems with Applications, 42(19):6578–6585.
Ha, Q., Liu, B., and Liu, F. (2020). Identifying melanoma images using efficientnet ensemble: Winning solution to the SIIM-ISIC melanoma classification challenge. Disponível em: https://arxiv.org/pdf/2010.05351.pdf. Último acesso em: 09 set. 2022.
Hasan, M. K., Dahal, L., Samarakoon, P. N., Tushar, F. I., and Martí, R. (2020). DSNet: Automatic dermoscopic skin lesion segmentation. Computers in Biology and Medicine, 120:103738.
Kawahara, J., Daneshvar, S., Argenziano, G., and Hamarneh, G. (2018). Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE Journal of Biomedical and Health Informatics, 23(2):538–546.
Ministério da Saúde (2019). Terapia-alvo (vemurafenibe, dabrafenibe, cobimetinibe, trametinibe) e imunoterapia (ipilimumabe, nivolumabe, pembrolizumabe) para o tratamento de primeira linha do melanoma avançado não-cirúrgico e metastático. CONITEC.
Pacheco, A. G., Trappenberg, T., and Krohling, R. A. (2020). Learning dynamic weights for an ensemble of deep models applied to medical imaging classification. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 1–8.
Qiu, Q., Patel, V. M., and Chellappa, R. (2014). Information-theoretic dictionary learning for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(11):2173–2184.
Scharcanski, J. and Celebi, M. E. (2014). Computer Vision Techniques for the Diagnosis of Skin Cancer. Springer.
Woza, M. (2018). Skin lesion analyzer. Disponível em: http://skin.test.woza.work/. Último acesso em: 09 set. 2022.
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., and Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2):210–227.
Zhang, J., Li, W., Ogunbona, P., and Xu, D. (2019). Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective. ACM Computing Surveys, 52(1):1–38.
Publicado
27/06/2023
Como Citar
FLORES, Eliezer Soares; SCHARCANSKI, Jacob.
Representações Esparsas Através de Dicionários para Processamento e Análise de Imagens Macroscópicas de Lesões Melanocíticas. In: PRÊMIO ARTUR ZIVIANI - CONCURSO DE TESES E DISSERTAÇÕES (DOUTORADO) - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 144-149.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2023.229522.
