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
this https URL

本文提出了一种基于文本信息的显式对比学习机制，通过使用文本编码器特征来学习更强大的特征表示，以增强模型对数据的理解，从而在不同的分割任务中实现了更强大的性能。