MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder
This work addresses the problem of limited data and computational bottlenecks in medical diagnostics, offering an incremental advancement in multimodal self-supervised learning for this domain.
The paper tackles the challenge of intermodality learning with Masked Autoencoders in medical analysis by introducing MedFLIP, a fast pre-training method that uses masking and an SVD loss to enhance representation learning, showing improved zero-shot performance and efficient scaling for medical image analysis.
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.