增强放射影像报告基础模型中的表示能力：一种基于掩码对比学习的细粒度对齐算法
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
AI总结：
本文提出掩码对比胸部X光基础模型MaCo，通过相关性加权机制实现细粒度图像理解与零样本学习，在6个数据集上超越10种最先进方法，覆盖分类、分割、检测和短语定位任务。
摘要：
近年来，多模态视觉-语言基础模型在医学领域引起了广泛关注。尽管这些模型提供了巨大的机遇，但它们仍面临关键挑战，例如在计算机辅助诊断中需要细粒度的知识理解，以及在真实临床应用中利用极少量甚至没有任务特定标注数据的能力。在本研究中，我们提出了 MaCo，一种掩码对比胸部 X 光基础模型，以应对这些挑战。MaCo 探索了掩码对比学习，以同时实现细粒度图像理解和针对多种医学影像任务的零样本学习。它设计了一种相关性加权机制，用于调整被掩码的胸部 X 光图像块与其对应报告之间的相关性，从而增强模型的表示学习能力。为评估 MaCo 的性能，我们使用 6 个知名的开源 X 光数据集进行了大量实验。实验结果表明，MaCo 在分类、分割、检测和短语定位等任务上优于 10 种最先进的方法。这些发现凸显了 MaCo 在推动广泛的医学图像分析任务方面的巨大潜力。
英文摘要：
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges. MaCo explores masked contrastive learning to simultaneously achieve fine-grained image understanding and zero-shot learning for a variety of medical imaging tasks. It designs a correlation weighting mechanism to adjust the correlation between masked chest X-ray image patches and their corresponding reports, thereby enhancing the model's representation learning capabilities. To evaluate the performance of MaCo, we conducted extensive experiments using 6 well-known open-source X-ray datasets. The experimental results demonstrate the superiority of MaCo over 10 state-of-the-art approaches across tasks such as classification, segmentation, detection, and phrase grounding. These findings highlight the significant potential of MaCo in advancing a wide range of medical image analysis tasks.
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。
