OTCXR：基于最优传输重新思考胸部X光分析的的自监督对齐
OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis
AI总结：
本文提出OTCXR框架，通过最优传输与跨视角语义注入模块学习密集语义不变性，并结合方差与协方差正则化，在胸部X光分析中超越现有自监督方法。
摘要：
自监督学习（SSL）因其无需标注即可学习的能力，已成为分析X射线等医学模态的一种有前景的技术。然而，传统SSL方法在实现语义对齐和捕捉细微细节方面面临挑战，这限制了它们准确表示底层解剖结构和病理特征的能力。为解决这些局限性，我们提出了OTCXR，一种利用最优传输（OT）学习密集语义不变性的新型SSL框架。通过将OT与我们创新的跨视角语义注入模块（CV-SIM）相结合，OTCXR增强了模型不仅捕捉局部空间特征，而且捕捉跨不同视角的全局上下文依赖关系的能力。该方法提升了SSL在胸部X光片背景下的有效性。此外，OTCXR在OT框架内引入方差和协方差正则化，以优先考虑临床相关信息，同时抑制信息量较少的特征。这确保了所学表示具有全面性和判别性，尤其有利于胸部疾病诊断等任务。我们通过在三个公开可用的胸部X光数据集上的综合实验验证了OTCXR的有效性。我们的实证结果表明，OTCXR在所有评估任务上均优于最先进的方法，证实了其学习语义丰富表示的能力。
英文摘要：
Self-supervised learning (SSL) has emerged as a promising technique for analyzing medical modalities such as X-rays due to its ability to learn without annotations. However, conventional SSL methods face challenges in achieving semantic alignment and capturing subtle details, which limits their ability to accurately represent the underlying anatomical structures and pathological features. To address these limitations, we propose OTCXR, a novel SSL framework that leverages optimal transport (OT) to learn dense semantic invariance. By integrating OT with our innovative Cross-Viewpoint Semantics Infusion Module (CV-SIM), OTCXR enhances the model's ability to capture not only local spatial features but also global contextual dependencies across different viewpoints. This approach enriches the effectiveness of SSL in the context of chest radiographs. Furthermore, OTCXR incorporates variance and covariance regularizations within the OT framework to prioritize clinically relevant information while suppressing less informative features. This ensures that the learned representations are comprehensive and discriminative, particularly beneficial for tasks such as thoracic disease diagnosis. We validate OTCXR's efficacy through comprehensive experiments on three publicly available chest X-ray datasets. Our empirical results demonstrate the superiority of OTCXR over state-of-the-art methods across all evaluated tasks, confirming its capability to learn semantically rich representations.
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。
