Scientific journals

Pereira, D. C., Silva, A. B., Longo, L. H., Loyola, A. M., Cardoso, S. V., de Faria, P. R., ... & Nascimento, M. Z. (2026). Evaluation of fractal descriptors, deep features and XAI representations with LASSO-regularized Hermite polynomial classifier for H&E histological image classification. Biomedical Signal Processing and Control, 112, 108393. https://doi.org/10.1016/j.bspc.2025.108393
Roberto, G. F., Pereira, D. C., Martins, A. S., Tosta, T. A., Soares, C., Lumini, A., ... & Nascimento, M. Z. (2025). Exploring percolation features with polynomial algorithms for classifying Covid-19 in chest X-ray images. Pattern Recognition Letters, 189, 248-255. https://doi.org/10.1016/j.patrec.2024.07.022
Miguel, P. L., Neves, L. A., Lumini, A., Medalha, G. C., Roberto, G. F., Rozendo, G. B., ... & do Nascimento, M. Z. (2025). Entropy-Regularized Attention for Explainable Histological Classification with Convolutional and Hybrid Models. Entropy, 27(7), 722. https://doi.org/10.3390/e27070722
Oliveira, D. L. L., Tosta, T. A. A., Neves, L. A. & Nascimento, M. Z. do. Hybrid CNN-transformer models in histopathology image analysis: A scoping review. IEEE Access, v. 1, p. 1-1, 2025. https://doi.org/10.1109/ACCESS.2025.3644767
Rozendo, G. B., Garcia, B. L. D. O., Borgue, V. A. T., Lumini, A., Tosta, T. A. A., Nascimento, M. Z. D., & Neves, L. A. (2024). Data augmentation in histopathological classification: An analysis exploring gans with xai and vision transformers. Applied Sciences, 14(18), 8125. https://doi.org/10.3390/app14188125
Roberto, G. F., Neves, L. A., Lumini, A., Martins, A. S., & Nascimento, M. Z. D. (2024). An ensemble of learned features and reshaping of fractal geometry-based descriptors for classification of histological images. Pattern Analysis and Applications, 27(1), 8. https://doi.org/10.1007/s10044-024-01223-w
Tenguam, J. J., da Costa Longo, L. H., Roberto, G. F., Tosta, T. A., Silva, A. B., do Nascimento, M. Z., & Neves, L. A. (2024). Higuchi Fractal Dimension with a multidimensional approach for color images. Software Impacts, 21, 100690. https://doi.org/10.1016/j.simpa.2024.100690 [source code1, source code2]
Silva, A. B., Martins, A. S., Tosta, T. A. A., Loyola, A. M., Cardoso, S. V., Neves, L. A., ... & do Nascimento, M. Z. (2024). OralEpitheliumDB: A dataset for oral epithelial dysplasia image segmentation and classification. Journal of Imaging Informatics in Medicine, 1-20. https://doi.org/10.1007/s10278-024-01041-w [dataset]
Tenguam, J. J., Longo, L. H. D. C., Roberto, G. F., Tosta, T. A., de Faria, P. R., Loyola, A. M., ... & Neves, L. A. (2024). Ensemble learning-based solutions: An approach for evaluating multiple features in the context of H&E histological images. Applied Sciences, 14(3), 1084. https://doi.org/10.3390/app14031084
Tosta, T. A. A., de Faria, P. R., Neves, L. A., Martins, A. S., Kaushal, C., & do Nascimento, M. Z. (2024). Evaluation of sparsity metrics and evolutionary algorithms applied for normalization of H&E histological images. Pattern Analysis and Applications, 27(1), 11. https://doi.org/10.1007/s10044-024-01218-7 [source code]
Martinez, J. M. C., Neves, L. A., Longo, L. H. D. C., Rozendo, G. B., Roberto, G. F., Tosta, T. A. A., ... & do Nascimento, M. Z. (2024). Exploring DeepDream and XAI representations for classifying histological images. SN Computer Science, 5(4), 362. https://doi.org/10.1007/s42979-024-02671-1
de Oliveira, C. I., do Nascimento, M. Z., Roberto, G. F., Tosta, T. A., Martins, A. S., & Neves, L. A. (2024). Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier. Multimedia Tools and Applications, 83(8), 21929-21952. https://doi.org/10.1007/s11042-023-16351-4
Dos Santos, D. F., de Faria, P. R., Travençolo, B. A., & do Nascimento, M. Z. (2023). Influence of data augmentation strategies on the segmentation of Oral histological images using fully convolutional neural networks. Journal of Digital Imaging, 36(4), 1608-1623. https://doi.org/10.1007/s10278-023-00814-z
Tosta, T. A. A., Freitas, A. D., de Faria, P. R., Neves, L. A., Martins, A. S., & do Nascimento, M. Z. (2023). A stain color normalization with robust dictionary learning for breast cancer histological images processing. Biomedical Signal Processing and Control, 85, 104978. https://doi.org/10.1016/j.bspc.2023.104978 [source code]
Longo, L. H. D. C., Roberto, G. F., Tosta, T. A., de Faria, P. R., Loyola, A. M., Cardoso, S. V., ... & Neves, L. A. (2023). Classification of multiple H&E images via an ensemble computational scheme. Entropy, 26(1), 34. https://doi.org/10.3390/e26010034
Miguel, J. P. M., Neves, L. A., Martins, A. S., do Nascimento, M. Z., & Tosta, T. A. A. (2023). Analysis of neural networks trained with evolutionary algorithms for the classification of breast cancer histological images. Expert Systems with Applications, 120609. https://doi.org/10.1016/j.eswa.2023.120609
Roberto, G. F., Neves, L. A., da Costa Longo, L. H., Rozendo, G. B., Tosta, T. A. A., de Faria, P. R., ... & do Nascimento, M. Z. (2022). Percolation Features: An approach for evaluating fractal properties in colour images. Software Impacts, 14, 100387. https://doi.org/10.1016/j.simpa.2022.100387 [source code]
Rozendo, G. B. ; Nascimento, M. Z.; Roberto, G. F.; Faria, P. R.; Tosta, T. A. A.; Silva, A. B.; Neves, L. A. Classification of non-hodgkin lymphomas based on sample entropy signatures. Expert Systems With Applications, 2022. https://doi.org/10.1016/j.eswa.2022.117238
Silva, A. B., Martins, A. S., Tosta, T. A. A., Neves, L. A., Servato, J. P. S., de Araújo, M. S., ... & do Nascimento, M. Z. (2022). Computational analysis of histological images from hematoxylin and eosin-stained oral epithelial dysplasia tissue sections. Expert Systems with Applications, 193, 116456. https://doi.org/10.1016/j.eswa.2021.116456
Rozendo, G. B. ; Nascimento, M. Z.; Roberto, G. F.; Faria, P. R.; Tosta, T. A. A.; Silva, A. B.; Neves, L. A. Sample entropy signatures: A new way to interpret SampEn values. Software Impacts, 2022. https://doi.org/10.1016/j.simpa.2022.100329 [source code]
Roberto, G. F. ; Lumini, A.; Neves, L. A.; Nascimento, M. Z. Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images. Expert Systems With Applications, v. 166, p. 114103, 2021. https://doi.org/10.1016/j.eswa.2020.114103
Tosta, T. A. A.; Faria, P. R. ; Neves, L. A.; Nascimento, M. Z. Evaluation of statistical and Haralick texture features for lymphoma histological images classification. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, v. 1, p. 1-12, 2021. https://doi.org/10.1080/21681163.2021.1902401
Taino, D. F.; Ribeiro, M. G.; Roberto, G. F.; Zafalon, G. F. D.; Nascimento, M. Z.; Tosta, T. A. A.; Martins, A. S.; Neves, L. A. Analysis of cancer in histological images: employing an approach based on genetic algorithm. Pattern Analysis And Applications, v. 24, p. 483-496, 2021. https://doi.org/10.1007/s10044-020-00931-3
Martins, A. S.; Neves, L. A.; De Faria, P R.; Tosta, T. A. 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, v. 24, p. 523-535, 2021. https://doi.org/10.1007/s10044-020-00927-z
Ribeiro, M. G.; Neves, L. A.; Nascimento, M. Z; Roberto, G. F.; Martins, A. S.; Azevedo Tosta, T. A. Classification of colorectal cancer based on the association of multidimensional and multiresolution features. Expert Systems With Applications, v. 120, p. 262-278, 2019. https://doi.org/10.1016/j.eswa.2018.11.034
Azevedo Tosta, T. A.; De Faria, P. R.; Neves, L. A.; Nascimento, M. Z. Computational normalization of H&E-stained histological images: Progress, challenges and future potential. Artificial Intelligence In Medicine, v. 95, p. 118-132, 2019. https://doi.org/10.1016/j.artmed.2018.10.004
Tosta, T. A. A.; De Faria, P. R.; Neves, L. A.; Nascimento, M. Z. Color normalization of faded H&E-stained histological images using spectral matching. Computers In Biology And Medicine, v. 111, p. 103344, 2019. https://doi.org/10.1016/j.compbiomed.2019.103344
Azevedo Tosta, T. A.; De Faria, P. R.; Silva Servato, J. P.; Neves, L. A.; Roberto, G. F.; Martins, A. S.; Nascimento, M. Z. Unsupervised method for normalization of hematoxylin-eosin stain in histological images. Computerized Medical Imaging And Graphics, v. 77, p. 101646, 2019. https://doi.org/10.1016/j.compmedimag.2019.101646
Roberto, G. F.; Nascimento, M. Z.; Martins, A. S.; Tosta, T. A. A.; Faria, P. R.; Neves, L. A. Classification of breast and colorectal tumors based on percolation of color normalized images. Computers & Graphics-UK, v. 84, p. 134-143, 2019. https://doi.org/10.1016/j.cag.2019.08.008
Nascimento, M. Z.; Martins, A. S.; Azevedo Tosta, T. A.; Neves, L. A. Lymphoma images analysis using morphological and non-morphological descriptors for classification. Computer Methods And Programs In Biomedicine, v. 163, p. 65-77, 2018. https://doi.org/10.1016/j.cmpb.2018.05.035
Azevedo Tosta, T. A.; Faria, P. R.; Batista, V. R.; Neves, L. A.; Do Nascimento, M. Z. Using wavelet sub-band and fuzzy 2-partition entropy to segment chronic lymphocytic leukemia images. Applied Soft Computing, v. 64, p. 49-58, 2018. https://doi.org/10.1016/j.asoc.2017.11.039
Segato Dos Santos, L. F.; Neves, L. A.; Rozendo, G. B.; Ribeiro, M. G.; Nascimento, M. Z.; Azevedo Tosta, T. A. Multidimensional and fuzzy sample entropy (SampEnMF) for quantifying H&E histological images of colorectal cancer. Computers In Biology And Medicine, v. 103, p. 148-160, 2018. https://doi.org/10.1016/j.compbiomed.2018.10.013
Azevedo Tosta, T. A.; Faria, P. R.; Alves Neves, L.; Nascimento, M. Z. Computational method for unsupervised segmentation of lymphoma histological images based on fuzzy 3-partition entropy and genetic algorithm. Expert Systems With Applications, v. 81, p. 223-243, 2017. https://doi.org/10.1016/j.eswa.2017.03.051
Azevedo Tosta, T. A.; Neves, L. A.; Nascimento, M. Z. Segmentation methods of H&E-stained histological images of lymphoma: A review. Informatics in Medicine Unlocked, v. 9, p. 35-43, 2017. https://doi.org/10.1016/j.imu.2017.05.009
Roberto, G. F.; Neves, L. A.; Nascimento, M. Z.; Tosta, T. A. 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, p. 135-147, 2017. https://doi.org/10.1016/j.compbiomed.2017.10.012

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