标题： Diff-Lung：基于扩散的纹理合成在肺部CT扫描病理组织分割中的增强技术 显示英文标题

标题： Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological Tissue Segmentation in Lung CT Scans

摘要： 准确量化肺部病理模式（纤维化、磨玻璃影、肺气肿、实变）的程度是间质性肺疾病诊断和随访的前提。 然而，由于健康组织与病理组织之间存在显著的类别不平衡，分割具有挑战性。 本文通过利用扩散模型进行数据增强来解决这一问题，该方法应用于训练AI模型过程中。 我们的方法生成合成的病理组织切片，同时保留每种组织类型特有的基本形状特征和复杂细节。 这种方法通过增加训练数据中代表性不足类别的出现次数来增强分割过程。 我们证明了基于扩散的数据增强技术提高了所有病理组织类型的分割准确性，特别是对于那些较少见的模式。 这一进步有助于更可靠的肺部CT扫描自动化分析，可能改善临床决策和患者预后。 显示英文摘要

摘要： Accurate quantification of the extent of lung pathological patterns (fibrosis, ground-glass opacity, emphysema, consolidation) is prerequisite for diagnosis and follow-up of interstitial lung diseases. However, segmentation is challenging due to the significant class imbalance between healthy and pathological tissues. This paper addresses this issue by leveraging a diffusion model for data augmentation applied during training an AI model. Our approach generates synthetic pathological tissue patches while preserving essential shape characteristics and intricate details specific to each tissue type. This method enhances the segmentation process by increasing the occurence of underrepresented classes in the training data. We demonstrate that our diffusion-based augmentation technique improves segmentation accuracy across all pathological tissue types, particularly for the less common patterns. This advancement contributes to more reliable automated analysis of lung CT scans, potentially improving clinical decision-making and patient outcomes