MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training
  with Masked Autoencoder
Lei Li Tianfang Zhang Xinglin Zhang Jiaqi Liu Bingqi Ma Yan Luo Tao Chen
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 ability to learn from limited data, a common scenario in
medical diagnostics. We verify that masking an image does not affect intermodal
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.
Lastly, we validate using language will improve the zero-shot performance for
the medical image analysis. MedFLIP 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,
setting an explored standard for future research and application in medical
diagnostics.