LViT: Language meets Vision Transformer in Medical Image Segmentation

Li, Zihan; Li, Yunxiang; Li, Qingde; Wang, Puyang; Guo, Dazhou; Lu, Le; Jin, Dakai; Zhang, You; Hong, Qingqi

Abstract

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.

Citation

Li, Z., Li, Y., Li, Q., Wang, P., Guo, D., Lu, L., Jin, D., Zhang, Y., & Hong, Q. (2024). LViT: Language meets Vision Transformer in Medical Image Segmentation. IEEE Transactions on Medical Imaging, 43(1), 96-107. https://doi.org/10.1109/TMI.2023.3291719

• Journal Article Type: Article
• Acceptance Date: Jul 3, 2023
• Online Publication Date: Jul 3, 2023
• Publication Date: 2024-01
• Deposit Date: Jan 19, 2024
• Publicly Available Date: Feb 6, 2024
• Journal: IEEE Transactions on Medical Imaging
• Print ISSN: 0278-0062
• Electronic ISSN: 1558-254X
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 43
• Issue: 1
• Pages: 96-107
• DOI: https://doi.org/10.1109/TMI.2023.3291719
• Keywords: Vision-language; Medical image segmentation; Semi-supervised learning
• Public URL: https://hull-repository.worktribe.com/output/4338534