MedFLIP：基于掩码自编码器的医学视觉-语言自监督快速预训练
MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder
AI总结：
本文提出MedFLIP，一种基于掩码自编码器的医学视觉-语言自监督快速预训练方法，通过跨域零样本学习和SVD损失增强医学图像特征表示，验证了掩码和语言对零样本性能的提升，为医学诊断提供高效精确的分析途径。
摘要：
在医学分析领域，大量研究探讨了掩码自编码器（MAEs）与多模态数据之间相互学习的潜力。然而，MAEs对模态间的影响仍是一个关键挑战。我们提出了MedFLIP，一种用于医学分析的快速语言-图像预训练方法。我们探索了MAEs在跨域零样本学习中的应用，这增强了模型从有限数据中学习的能力，而这是医学诊断中的常见场景。我们验证了掩码图像不会影响模态间学习。此外，我们提出了SVD损失以增强医学图像特征的表示学习，旨在通过利用此类数据的结构复杂性来提高分类准确率。我们的理论假设，掩码促进了语义保持、鲁棒特征提取、正则化、域适应和不变性学习。最后，我们验证了使用语言将提升医学图像分析的零样本性能。MedFLIP对掩码过程的扩展标志着该领域的进步，为快速且精确的医学图像分析提供了一条途径，避免了传统的计算瓶颈。通过实验和验证，MedFLIP展现了高效的性能提升，有助于未来医学诊断的研究与应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
