A Method For Multiclass Lymphoma Classification Based on Morphological and Non-Morphological Descriptors
Resumo
Lymphoma is one of the most common types of cancer and its treatment can be more effective if the disease variant is correctly diagnosed. Many works have been done using computer vision and machine learning to classify the images. This work presents lymphoma based on histological a method using simple descriptors and a decision tree-based ensemble classifier, aiming to maintaing the interpretability of the data and understand what information in most important to the classification task. We use morphological and non morphological descriptors extracted from the cells nuclei, a feature selection method based on principal component analysis (PCA), and a gradient boosting decision tree (GBDT) method for multiclass classification. Our approach achieves an average accuracy of 0.932. this result is close to those obtained in the state of the art, while it uses simpler descriptors and better interpretable classification models.
Palavras-chave:
Multiclass classification, feature selection, morphological and non-morphological descriptors, lymphoma
Referências
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Welch, B.L.: The generalization of ”student's' problem when several different population variances are involved. Biometrika pp. 28-35 (1947)
Yuan, Y., Li, S., Zhang, X., Sun, J.: A comparative analysis of svm, naive bayes and gbdt for data faults detection in wsns. pp. 394-399 (2018)
Aggarwal, C.C.: Data mining: the textbook (2015)
Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al.: Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information Fusion 58, 82-115 (2020)
Bai, J., Jiang, H., Li, S., Ma, X.: Nhl pathological image classification based on hierarchical local information and googlenet-based representations. BioMed research Int. (2019)
Beck, A.H., et al.: Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. (2011)
Codella, N., Moradi, M., Matasar, M., Sveda-Mahmood, T., Smith, J.R.: Lymphoma diagnosis in histopathology using a multi-stage visual learning approach. In: Medical Imaging 2016: Digital Pathology. vol. 9791, p. 97910H (2016)
Deng, H.: Interpreting tree ensembles with intrees. Int. J. of Data Science and Analytics 7(4), 277-287 (2019)
Derrick, B., Toher, D., White, P.: Why welch's test is type i error robust. The Quantitative Methods in Psychology (2016)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An introduction to statistical learning, vol. 112. Springer (2013)
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: Lightgbm: A highly efficient gradient boosting decision tree. In: Advances in neural information processing systems. pp. 3146-3154 (2017)
Li, Z., et al.: Gbdt-svm credit risk assessment model and empirical analysis of peer-to-peer borrowers under consideration of audit information. Open Journal of Business and Management 6(02), 362 (2018)
Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., Lee, S.I.: From local explanations to global understanding with explainable ai for trees. Nature machine intelligence 2(1), 56-67 (2020)
Martins, A.S., Neves, L.A., de Faria, P.R., Tosta, T.A., Longo, L.C., Silva, A.B., Roberto, G.F., do Nascimento, M.Z.: A hermite polynomial algorithm for detection of lesions in lymphoma images. Pattern Analysis and Applications pp. 1-13 (2020)
Meng, T., Lin, L., Shyu, M.L., Chen, S.C.: Histology image classification using supervised classification and multimodal fusion. In: 2010 IEEE Int. symposium on multimedia. pp. 145-152 (2010)
Nannia, L., Ghidoni, S., Brahnam, S.: Ensemble of convolutional neural networks for bioimage classification. Applied Computing and Informatics (2020)
Nascimento, M.Z., Neves, L., Duarte, S.C., Duarte, Y.A.S., Batista, V.R.: Classification of histological images based on the stationary wavelet transform. J. of Physics: Conference Series 574, 012133 (jan 2015)
do Nascimento, M.Z., Martins, A.S., Tosta, T.A.A., Neves, L.A.: Lymphoma images analysis using morphological and non-morphological descriptors for classification. Computer methods and programs in biomedicine 163, 65-77 (2018)
Orlov, N.V., Chen, W.W., Eckley, D.M., Macura, T.J., Shamir, L., Jaffe, E.S., Goldberg, I.G.: Automatic classification of lymphoma images with transform-based global features. IEEE Trans. Inf Technol Biomed 14(4), 1003-1013 (2010)
Oza, N.C., Tumer, K.: Classifier ensembles: Select real-world applications. Information fusion pp. 4-20 (2008)
Roberto, G.F., Neves, L.A., Nascimento, M.Z., Tosta, T.A., Longo, L.C., Martins, A.S., Faria, P.R.: Features based on the percolation theory for quantification of non-hodgkin lymphomas. Computers in biology and medicine 91, 135-147 (2017)
Russell, S., Norvig, P.: Artificial intelligence: a modern approach
Santos, F.P.S., Fernandes, G.S.: Linfomas não-Hodgkin. MedicinaNet (2008)
Shamir, L., Orlov, N., Eckley, D.M., Macura, T.J., Goldberg, I.G.: Iicbu 2008: a proposed benchmark suite for biological image analysis. Medical & biological engineering & computing 46(9), 943-947 (2008)
Song, F., Guo, Z., Mei, D.: Feature selection using principal component analysis. In: Int. Conf. on System Science, Engineering Design and Manufacturing Informatization. vol. 1, pp. 27-30 (2010)
Song, Y., Cai, W., Huang, H., Feng, D., Wang, Y., Chen, M.: Bioimage classification with subcategory discriminant transform of high dimensional visual descriptors. BMC bioinformatics 17(1), 465 (2016)
Steinbuss, G., Kriegsmann, M., Zgorzelski, C., Brobeil, A., Goeppert, B., Dietrich, S., Mechtersheimer, G., Kriegsmann, K.: Deep learning for the classification of non-hodgkin lymphoma on histopathological images. Cancers 13(10), 2419 (2021)
Sun, R., Wang, G., Zhang, W., Hsu, L.T., Ochieng, W.Y.: A gradient boosting decision tree based gps signal reception classification algorithm. Applied Soft Computing 86 (2020)
Welch, B.L.: The generalization of ”student's' problem when several different population variances are involved. Biometrika pp. 28-35 (1947)
Yuan, Y., Li, S., Zhang, X., Sun, J.: A comparative analysis of svm, naive bayes and gbdt for data faults detection in wsns. pp. 394-399 (2018)
Publicado
22/11/2021
Como Citar
FARIA, Tiago P. de; NASCIMENTO, Marcelo Z. do; MARTINS, Luiz G. A..
A Method For Multiclass Lymphoma Classification Based on Morphological and Non-Morphological Descriptors. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 184-189.
DOI: https://doi.org/10.5753/wvc.2021.18911.
