  Deep learning has been widely used in medical image segmentation and other
aspects. However, the performance of existing medical image segmentation models
has been limited by the challenge of obtaining sufficient high-quality labeled
data due to the prohibitive data annotation cost. To alleviate this limitation,
we propose a new text-augmented medical image segmentation model LViT (Language
meets Vision Transformer). In our LViT model, medical text annotation is
incorporated to compensate for the quality deficiency in image data. In
addition, the text information can guide to generate pseudo labels of improved
quality in the semi-supervised learning. We also propose an Exponential Pseudo
label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM)
preserve local image features in semi-supervised LViT setting. In our model, LV
(Language-Vision) loss is designed to supervise the training of unlabeled
images using text information directly. For evaluation, we construct three
multimodal medical segmentation datasets (image + text) containing X-rays and
CT images. Experimental results show that our proposed LViT has superior
segmentation performance in both fully-supervised and semi-supervised setting.
The code and datasets are available at https://github.com/HUANGLIZI/LViT.
