SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Mariana Aya Suzuki Uchida(mariaya25@usp.br)
2 Afonso Sousa Lima(afonso.matheus@usp.br)
3 Elaine Sousa(parros@icmc.usp.br)
4 Agma Traina(agma@icmc.usp.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Chen, K., Sun, S., Zhao, J., Wang, F., and Zhang, Q. (2025). Multi-view representation for pathological image classification via contrastive learning. International Journal of Machine Learning and Cybernetics, 16(4):2285–2296.
2 Chengxiao, Y., Xiaoyang, Z., Ahmed, A., Tunio, M. H., and Shuhuan, F. (2023). Diffusion-enhanced magnified histopathological images for robust breast cancer classification. In 2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pages 1–7. IEEE.
3 Durgamahanthi, V., Anita Christaline, J., and Shirly Edward, A. (2021). Glcm and glrlm based texture analysis: Application to brain cancer diagnosis using histopathology images. In Dash, S. S., Das, S., and Panigrahi, B. K., editors, Intelligent Computing and Applications, page 691–706, Singapore. Springer
4 Hesamian, M. H., Jia, W., He, X., and Kennedy, P. (2019). Deep learning techniques for medical image segmentation: Achievements and challenges. Journal of Digital Imaging, 32(4):582–596.
5 Kumar, T., Brennan, R., Mileo, A., and Bendechache, M. (2024). Image data augmentation approaches: A comprehensive survey and future directions. IEEE Access, 12:187536–187571.
6 Li, Y., Yu, Y., Zou, Y., Xiang, T., and Li, X. (2022). Online easy example mining for weakly-supervised gland segmentation from histology images. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 578– 587. Springer.
7 Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll´ar, P. (2017). Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2999–3007.
8 Liu, L., Liang, Y., Yan, X., Huangfu, L., Samtani, S., Yu, Z., Zhang, Y., and Zeng, D. D. (2025a). Hard sample mining: A new paradigm of efficient and robust model training. IEEE Transactions on Neural Networks and Learning Systems, page 1–21.
9 Liu, L., Zhang, P., Liang, Y., Liu, J., Morra, L., Guo, B., Yu, Z., Zhang, Y., and Zeng, D. D. (2025b). $\gamma$-Razor: Hardness-Aware Dataset Pruning for Efficient Neural Network Training. IEEE Transactions on Computational Social Systems, 12(3):957–971.
10 Peta, J. and Koppu, S. (2024). Explainable soft attentive efficientnet for breast cancer classification in histopathological images. Biomedical Signal Processing and Control, 90:105828.
11 Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618–626.
12 Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7):1455–1462.
13 Xue, C., Dou, Q., Shi, X., Chen, H., and Heng, P.-A. (2019). Robust learning at noisy labeled medical images: Applied to skin lesion classification. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), page 1280–1283.
14 S¸ aban ¨Ozt¨urk and Akdemir, B. (2018). Application of feature extraction and classification methods for histopathological image using glcm, lbp, lbglcm, glrlm and sfta. Procedia Computer Science, 132:40–46. International Conference on Computational Intelligence and Data Science.