Textmatch: Using Text Prompts to Improve Semi-supervised Medical Image Segmentation

Semi-supervised learning, a paradigm involving training models with limited labeled data alongside abundant unlabeled images, has significantly ad-vanced medical image segmentation. However, the absence of label supervi-sion introduces noise during training, posing a challenge in achieving a well-clustered feature space essential for acquiring discriminative representations in segmentation tasks. In this context, the emergence of vision-language (VL) models in natural image processing has showcased promising capabili-ties in aiding object localization through the utilization of text prompts, demonstrating potential as an effective solution for addressing annotation scarcity. Building upon this insight, we present Textmatch, a novel frame-work that leverages text prompts to enhance segmentation performance in semi-supervised medical image segmentation. Specifically, our approach in-troduces a Bilateral Prompt Decoder (BPD) to address modal discrepancies between visual and linguistic features, facilitating the extraction of comple-mentary information from multi-modal data. Then, we propose the Multi-views Consistency Regularization (MCR) strategy to ensure consistency among multiple views derived from perturbations in both image and text domains, reducing the impact of noise and generating more reliable pseudo-labels. Furthermore, we leverage these pseudo-labels and conduct Pseudo-Label Guided Contrastive Learning (PGCL) in the feature space to encourage intra-class aggregation and inter-class separation between features and proto-types, thus enhancing the generation of more discriminative representations for segmentation. Extensive experiments on two publicly available datasets demonstrate that our framework outperforms previous methods employing image-only and multi-modal approaches, establishing a new state-of-the-art performance.