语言引导的领域泛化医学图像分割
Language Guided Domain Generalized Medical Image Segmentation
AI总结：
针对医学图像单源领域泛化中视觉特征易受虚假相关性影响的问题，提出利用文本编码器特征引导对比学习以对齐并学习鲁棒表示，并在跨模态、跨序列和跨站点分割任务中验证有效性。
摘要：
单源领域泛化（SDG）有望在真实临床环境中实现更可靠、更一致的图像分割，尤其是在医学领域，数据隐私和采集成本限制常常制约了多样化数据集的可用性。仅依赖视觉特征会妨碍模型有效适应不同领域的能力，这主要是因为图像特征中嵌入了虚假相关性和领域特定特征。在视觉特征之外引入文本特征，是增强模型对数据理解的一种潜在解决方案，因为它超越像素级信息，提供了有价值的上下文。描述解剖结构、其外观以及在各种成像模态间变化的文本线索，可以引导模型进行领域适应，最终促成更鲁棒、更一致的分割。本文提出一种显式利用文本信息的方法，通过引入由文本编码器特征引导的对比学习机制，以学习更鲁棒的特征表示。我们在多种场景下评估了文本引导的对比特征对齐技术的有效性，包括针对不同分割任务的跨模态、跨序列和跨站点设置。与文献中的现有方法相比，我们的方法取得了良好性能。代码和模型权重可在 https://github.com/ShahinaKK/LG_SDG.git 获取。
英文摘要：
Single source domain generalization (SDG) holds promise for more reliable and consistent image segmentation across real-world clinical settings particularly in the medical domain, where data privacy and acquisition cost constraints often limit the availability of diverse datasets. Depending solely on visual features hampers the model's capacity to adapt effectively to various domains, primarily because of the presence of spurious correlations and domain-specific characteristics embedded within the image features. Incorporating text features alongside visual features is a potential solution to enhance the model's understanding of the data, as it goes beyond pixel-level information to provide valuable context. Textual cues describing the anatomical structures, their appearances, and variations across various imaging modalities can guide the model in domain adaptation, ultimately contributing to more robust and consistent segmentation. In this paper, we propose an approach that explicitly leverages textual information by incorporating a contrastive learning mechanism guided by the text encoder features to learn a more robust feature representation. We assess the effectiveness of our text-guided contrastive feature alignment technique in various scenarios, including cross-modality, cross-sequence, and cross-site settings for different segmentation tasks. Our approach achieves favorable performance against existing methods in literature. Our code and model weights are available at https://github.com/ShahinaKK/LG_SDG.git.
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。
