  Within the domain of medical analysis, extensive research has explored the
potential of mutual learning between Masked Autoencoders(MAEs) and multimodal
data. However, the impact of MAEs on intermodality remains a key challenge. We
introduce MedFLIP, a Fast Language-Image Pre-training method for Medical
analysis. We explore MAEs for zero-shot learning with crossed domains, which
enhances the model's ability to learn from limited data, a common scenario in
medical diagnostics. We verify that masking an image does not affect
inter-modal learning. Furthermore, we propose the SVD loss to enhance the
representation learning for characteristics of medical images, aiming to
improve classification accuracy by leveraging the structural intricacies of
such data. Our theory posits that masking encourages semantic preservation,
robust feature extraction, regularization, domain adaptation, and invariance
learning. Lastly, we validate using language will improve the zero-shot
performance for the medical image analysis. MedFLIP's scaling of the masking
process marks an advancement in the field, offering a pathway to rapid and
precise medical image analysis without the traditional computational
bottlenecks. Through experiments and validation, MedFLIP demonstrates efficient
performance improvements, helps for future research and application in medical
diagnostics.
