bibtext: @InProceedings{ Li_Textmatch_MICCAI2024,
                   author = { Li, Aibing and Zeng, Xinyi and Zeng, Pinxian and Ding, Sixian and Wang, Peng and Wang, Chengdi and Wang, Yan },
                   title = { { Textmatch: Using Text Prompts to Improve Semi-supervised Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: Textmatch: Using Text Prompts to Improve Semi-supervised Medical Image Segmentation
authors: Li, Aibing Zeng, Xinyi Zeng, Pinxian Ding, Sixian Wang, Peng Wang, Chengdi Wang, Yan