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Authors
# Name
1 Gabriel dos Santos(gabrielbarbosadossantos@usp.br)
2 Ana Elisa Jorge(anajorge@icmc.usp.br)
3 Agma Traina(agma@icmc.usp.br)
4 Mirela Cazzolato(mirela@icmc.usp.br)

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Reference
# Reference
1 Blanco, G. et al. (2020). A superpixel-driven deep learning approach for the analysis of dermatological wounds. Computer Methods and Programs in Biomedicine, 183:105079. DOI: 10.1016/j.cmpb.2019.105079.
2 Cazzolato, M. T., Ramos, J. S., Rodrigues, L. S., Scabora, L. C., Chino, D. Y. T., Jorge, A. E. S., de Azevedo-Marques, P. M., Traina Jr., C., and Traina, A. J. M. (2021). The UTrack framework for segmenting and measuring dermatological ulcers through telemedicine. Computer Methods and Programs in Biomedicine. DOI: 10.1016/j.compbiomed.2021.104489.
3 Chino, D. Y. T. et al. (2018). ICARUS: Retrieving skin ulcer images through bag-of-signatures. In Proc. IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), pages 82–87. IEEE. DOI: 10.1109/CBMS.2018.00022.
4 Forti, J. K., Navarro, T. P., and dos Santos, A. L. (2026). Explainable deep learning for etiological classification of vascular ulcers using a hybrid convolutional neural network-transformer model. JVS-Vascular Insights, 4:100419. DOI: 10.1016/j.jvsvi.2026.100419.
5 GBD 2021 Decubitus Ulcers Collaborators (2025). Global, regional and national burden of decubitus ulcers in 204 countries and territories from 1990 to 2021: a systematic analysis based on the Global Burden of Disease study 2021. Frontiers in Public Health, 13:1494229. DOI: 10.3389/fpubh.2025.1494229.
6 Johnson, J., Douze, M., and J´egou, H. (2019). Billion-scale similarity search with gpus. IEEE Transactions on Big Data, 7(3):535–547. DOI: 10.1109/TBDATA.2019.2921572.
7 Lin, W., Zhao, Z., Zhang, X., Wu, C., Zhang, Y., Wang, Y., and Xie, W. (2023). PMC-CLIP: Contrastive language-image pre-training using biomedical documents. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, volume 14227 of Lecture Notes in Computer Science, pages 525–536. Springer. DOI: 10.1007/978-3-031-43993-3 51.
8 Oquab, M., Darcet, T., Moutakanni, T., et al. (2024). DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research. https://openreview.net/forum?id=a68SUt6zFt.
9 Radford, A. et al. (2021). Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning (ICML). PMLR. https://proceedings.mlr.press/v139/radford21a/radford21a.pdf.
10 Sen, C. K. (2023). Human wound and its burden: Updated 2022 compendium of estimates. Advances in Wound Care, 12(12):657–670. DOI: 10.1089/wound.2023.0007.
11 Tschannen, M. et al. (2025). SigLIP 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features. arXiv preprint arXiv:2502.14786.
12 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS), volume 30. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
13 Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., Wong, C., Tupini, A., Wang, Y., Mazzola, M., Shukla, S., Liden, L., Gao, J., Lungren, M. P., Naumann, T., Wang, S., Poon, H., et al. (2023). BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs. arXiv preprint arXiv:2303.00915.