Tokenizers play a crucial role in preprocessing and encoding input data into a suitable format for transformer architectures. While tokenization techniques have mainly been developed and optimized for natural language processing tasks, adapting them to handle medical images comes with unique challenges due to the complexity and specificity of these types of images. We aim to develop customized tokenizers for vision transformers tailored to medical imaging tasks with the goal to improve performance and explainability.