Dictionary-based Sparse Representations for Automatic Processing and Analysis of Melanocytic Skin Lesions in Macroscopic Images
Resumo
Melanoma is the most lethal type of skin cancer, since it is most prone to metastasis. Specifically, the rate of patients who survive at least five years after early stage diagnosis of this disease is over 99%. However, this rate decreases to about 25% if detection occurs only at the last stage. In this context, systems that assist in the early diagnosis of melanoma can play an extremely important role, especially in regions where access to dermatologists is poor. However, differentiating melanoma from benign melanocytic lesions can be a challenging task, even for experienced specialists. To address this problem, in this thesis, an automatic system is proposed for melanoma detection from a simple digital photograph, which is based on sparse representation models. The results presented by the proposed system are promising and suggest that it can potentially outperform state-of-the-art alternatives and even trained dermatologists.
Referências
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J. Kawahara, S. Daneshvar, G. Argenziano, and G. Hamarneh, “Sevenpoint checklist and skin lesion classification using multitask multimodal neural nets,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 538–546, 2018.
E. Bernart, E. S. Flores, and J. Scharcanski, “Macroscopic pigmented skin lesion prescreening,” in Encyclopedia of Biomedical Engineering, R. Narayan, Ed. Amsterdam: Elsevier, 2019, vol. 2, pp. 561–573.
P. G. Cavalcanti, J. Scharcanski, and C. B. O. Lopes, “Shading attenuation in human skin color images,” in Proceedings of the 6th International Symposium on Visual Computing (ISVC). Las Vegas, NV, USA: Springer, November 2010.
M. Zortea, E. Flores, and J. Scharcanski, “A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images,” Pattern Recognition, vol. 64, pp. 92–104, 2017.
E. Flores and J. Scharcanski, “Segmentation of melanocytic skin lesions using feature learning and dictionaries,” Expert Systems with Applications, vol. 56, pp. 300–309, 2016.
Q. Qiu, V. M. Patel, and R. Chellappa, “Information-theoretic dictionary learning for image classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 11, pp. 2173–2184, 2014.
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J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009.
E. Flores, M. Zortea, and J. Scharcanski, “Dictionaries of deep features for land-use scene classification of very high spatial resolution images,” Pattern Recognition, vol. 89, pp. 32–44, 2019.
C. M. Bishop, Pattern Recognition and Machine Learning, 1st ed. Singapore: Springer, 2006.
A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183–202, 2009.
J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” Journal of Machine Learning Research, vol. 7, no. 1, pp. 1–30, 2006.
J. Glaister, A. Wong, and D. A. Clausi, “Segmentation of skin lesions from digital images using joint statistical texture distinctiveness,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 4, pp. 1220–1230, 2014.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, June 2015.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, June 2017.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: a deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481–2495, 2017.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “U-Net: Convolutional networks for biomedical image segmentation,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Munich, Germany: Springer, June 2015.
T. G. Dietterich, “Approximate statistical tests for comparing supervised classification learning algorithms,” Neural Computation, vol. 10, no. 7, pp. 1895–1923, 1998.
F. C. Morgan, J. Duran, B. Fraile, P. S. Karia, J. Y. Lin, P. A. Ott, E. S. Ruiz, D. M. Wang, Y. Zhang, and C. D. Schmults, “A comparison of skin cancer screening and treatment costs at a massachusetts cancer center, 2008 versus 2013,” Journal of the American Academy of Dermatology, vol. 79, no. 5, pp. 921–928, 2018.
J. Scharcanski and M. E. Celebi, Computer Vision Techniques for the Diagnosis of Skin Cancer, 1st ed., ser. Series in BioEngineering. Berlin, Heidelberg: Springer, 2014.
R. D. Melamed, I. T. Aydin, G. S. Rajan, R. Phelps, D. N. Silvers, K. J. Emmett, G. Brunner, R. Rabadan, and J. T. Celebi, “Genomic characterization of dysplastic nevi unveils implications for diagnosis of melanoma,” Journal of Investigative Dermatology, vol. 137, no. 4, pp. 905–909, 2017.
J. F. Alcón, C. Ciuhu, W. ten Kate, A. Heinrich, N. Uzunbajakava, G. Krekels, D. Siem, and G. de Haan, “Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis,” IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 1, pp. 14–25, 2009.
M. Woza. Skin lesion analyzer. Available at: [link]. Accessed on: 09 sep. 2022.
J. Zhang, W. Li, P. Ogunbona, and D. Xu, “Recent advances in transfer learning for cross-dataset visual recognition: A problem-oriented perspective,” ACM Computing Surveys, vol. 52, no. 1, pp. 1–38, 2019.
Q. Ha, B. Liu, and F. Liu. Identifying melanoma images using efficientnet ensemble: Winning solution to the SIIM-ISIC melanoma classification challenge. Available at: [link]. Accessed on: 09 sep. 2022.
A. G. Pacheco, T. Trappenberg, and R. A. Krohling, “Learning dynamic weights for an ensemble of deep models applied to medical imaging classification,” in Proceedings of the 2020 IEEE International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8.
N. C. F. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler, and A. Halpern, “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 15th IEEE International Symposium on Biomedical Imaging (ISBI). Washington, DC, USA: IEEE, April 2018.
University of Edinburgh. Dermofit image library. Available at: [link]. Accessed on: 09 sep. 2022.
R. Amelard, A. Wong, and D. A. Clausi, “Extracting high-level intuitive features (hlif) for classifying skin lesions using standard camera images,” in Proceedings of the 9th Canadian Conference on Computer and Robot Vision (CRV). Toronto, ON, Canada: IEEE, May 2012.
I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman, and N. Petkov, “MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images,” Expert Systems with Applications, vol. 42, no. 19, pp. 6578–6585, 2015.
T. J. Brinker, A. Hekler, A. Hauschild, C. Berking, B. Schilling, A. H. Enk, S. Haferkamp, A. Karoglan, C. von Kalle, M. Weichenthal et al., “Comparing artificial intelligence algorithms to 157 german dermatologists: the melanoma classification benchmark,” European Journal of Cancer, vol. 111, pp. 30–37, 2019.
M. H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, S. M. R. Soroushmehr, K. Ward, and K. Najarian, “Skin lesion segmentation in clinical images using deep learning,” in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). Cancún, Mexico: IEEE, December 2016.
M. A. Al-Masni, M. A. Al-Antari, M.-T. Choi, S.-M. Han, and T.-S. Kim, “Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks,” Computer Methods and Programs in Biomedicine, vol. 162, pp. 221–231, 2018.
M. K. Hasan, L. Dahal, P. N. Samarakoon, F. I. Tushar, and R. Martí, “DSNet: Automatic dermoscopic skin lesion segmentation,” Computers in Biology and Medicine, vol. 120, p. 103738, 2020.
A. Bissoto, M. Fornaciali, E. Valle, and S. Avila, “(de) constructing bias on skin lesion datasets,” in Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Long Beach, CA, USA: IEEE, June 2019.
J. Kawahara, S. Daneshvar, G. Argenziano, and G. Hamarneh, “Sevenpoint checklist and skin lesion classification using multitask multimodal neural nets,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 538–546, 2018.
E. Bernart, E. S. Flores, and J. Scharcanski, “Macroscopic pigmented skin lesion prescreening,” in Encyclopedia of Biomedical Engineering, R. Narayan, Ed. Amsterdam: Elsevier, 2019, vol. 2, pp. 561–573.
P. G. Cavalcanti, J. Scharcanski, and C. B. O. Lopes, “Shading attenuation in human skin color images,” in Proceedings of the 6th International Symposium on Visual Computing (ISVC). Las Vegas, NV, USA: Springer, November 2010.
M. Zortea, E. Flores, and J. Scharcanski, “A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images,” Pattern Recognition, vol. 64, pp. 92–104, 2017.
E. Flores and J. Scharcanski, “Segmentation of melanocytic skin lesions using feature learning and dictionaries,” Expert Systems with Applications, vol. 56, pp. 300–309, 2016.
Q. Qiu, V. M. Patel, and R. Chellappa, “Information-theoretic dictionary learning for image classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 11, pp. 2173–2184, 2014.
D. D. Lee and H. S. Seung, “Learning the parts of objects by nonnegative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788–791, 1999.
J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000.
J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009.
E. Flores, M. Zortea, and J. Scharcanski, “Dictionaries of deep features for land-use scene classification of very high spatial resolution images,” Pattern Recognition, vol. 89, pp. 32–44, 2019.
C. M. Bishop, Pattern Recognition and Machine Learning, 1st ed. Singapore: Springer, 2006.
A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183–202, 2009.
J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” Journal of Machine Learning Research, vol. 7, no. 1, pp. 1–30, 2006.
J. Glaister, A. Wong, and D. A. Clausi, “Segmentation of skin lesions from digital images using joint statistical texture distinctiveness,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 4, pp. 1220–1230, 2014.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE, June 2015.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, June 2017.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: a deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2481–2495, 2017.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “U-Net: Convolutional networks for biomedical image segmentation,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Munich, Germany: Springer, June 2015.
T. G. Dietterich, “Approximate statistical tests for comparing supervised classification learning algorithms,” Neural Computation, vol. 10, no. 7, pp. 1895–1923, 1998.
Publicado
06/11/2023
Como Citar
FLORES, Eliezer Soares; SCHARCANSKI, Jacob.
Dictionary-based Sparse Representations for Automatic Processing and Analysis of Melanocytic Skin Lesions in Macroscopic Images. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 36. , 2023, Rio Grande/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 56-62.
DOI: https://doi.org/10.5753/sibgrapi.est.2023.27452.
