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.

翻译：在医学分析领域，已有大量研究探索掩码自编码器（MAE）与多模态数据间相互学习的潜力。然而，MAE对跨模态交互的影响仍是关键挑战。我们提出MedFLIP——一种面向医学分析的快速语言-图像预训练方法。通过跨域零样本学习探索MAE的应用，该方法增强了模型在医学诊断中常见有限数据场景下的学习能力。我们验证了图像掩码不会影响跨模态学习。此外，我们提出奇异值分解损失（SVD loss），以增强针对医学图像特征的表征学习，旨在利用此类数据的结构复杂性提升分类精度。最后，我们证明语言信息的引入能改进医学图像分析的零样本性能。MedFLIP对掩码过程的缩放标志着该领域的进步，为绕开传统计算瓶颈实现快速精准的医学图像分析提供了路径。通过实验验证，MedFLIP展现出高效的性能提升，为医学诊断领域的未来研究与应用设立了探索标准。