Vim-Med: a Vision Mamba-based Model for Pathology Classification in X-Ray Images - Pitthan, Gregory J.; Cordova, Lucas B. V.; Schein, Tatiana T.; Silva, Eduardo L.; Dutra, Gustavo A.; Almeida, Gustavo P.; Brião, Stephanie L.; Drews-Jr, Paulo L. J. (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16757, 2025)
There is a need to improve medical diagnostics in identifying rare diseases and analyzing unbalanced image data. This work presents Vim-Med, an adaptation of the Vision Mamba (Vim) architecture for pathology classification in X-ray images. To evaluate the model, a comparison was made with other Mamba models and Transformer architectures. The results show that in the Chest X-Ray dataset, Vim-Med achieved the best F1-score with 0.888. In the NIH CRX8 dataset, Vim-Med excelled at handling rare classes (Macro-F1 of 0.192). Vim-Med achieved the highest inference speed, corresponding to 125 FPS, and achieved a reduction of more than 50% in training time. Thus, the Vim-Med model is efficient in classifying pathologies in X-ray images.