用大模型生成的医学概念指导肺部病灶分割，提升边界精准度。
CGSM: Concept-Guided Segmentation Model for Precise Pulmonary Lesion Delineation
- 通过概念-视觉对齐模块激活图文匹配的语义信息。
- 在QaTa-COV19数据集上达91.59% Dice和84.49% mIoU。
- 适合需要高精度肺部病灶分割的临床辅助诊断场景。
准确分割肺部病灶对临床诊断与治疗至关重要。现有方法普遍缺乏任务相关的语义引导，因文本标注通常仅提供粗略定位，导致病灶边界分割不精确，尤其对小病灶表现不佳。为此，我们提出概念引导分割模型CGSM，将大语言模型生成并经临床审核的概念融入分割流程。具体地，设计概念-视觉对齐模块（CVAM），激活与视觉特征对齐的概念词元，增强文本与图像的交互；引入概念调制解码器（CM-Decoder），以CVAM输出的概念作为调制信号，实现图像与文本特征的自适应融合，提升分割精度。在两个公开数据集上的大量实验表明，CGSM达到领先性能，在QaTa-COV19数据集上实现91.59% Dice和84.49% mIoU，验证了其在肺部病灶分割中的有效性。
原文摘要 · Abstract (English)
Accurate segmentation of pulmonary lesions is essential for effective clinical diagnosis and treatment strategies. Existing segmentation approaches often lack task-specific semantic guidance, as text-based annotations typically offer coarse localization of lesions, leading to inadequate delineation of lesion boundaries and poor performance on small-scale lesions. To address this, we propose CGSM, a Concept-Guided Segmentation Model that integrates LLM-generated and clinically reviewed concepts into the segmentation process. Specifically, we design a Concept-Visual Alignment Module (CVAM) to activate relevant tokens within the concepts that align with visual features, enhancing the interaction between textual and visual information. In addition, we introduce a Concept Modulated Decoder (CM-Decoder), which uses concepts from CVAM as modulation signals to facilitate the adaptive fusion of image and text features, improving the segmentation accuracy. Extensive experiments on two public datasets show that CGSM achieves state-of-the-art performance, with results of 91.59% Dice and 84.49% mIoU on the QaTa-COV19 dataset, demonstrating its effectiveness in pulmonary lesion segmentation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。