Kavli Affiliate: Yi Zhou
| First 5 Authors: Kaiwen Huang, Kaiwen Huang, , ,
| Summary:
Semi-supervised medical image segmentation is a crucial technique for
alleviating the high cost of data annotation. When labeled data is limited,
textual information can provide additional context to enhance visual semantic
understanding. However, research exploring the use of textual data to enhance
visual semantic embeddings in 3D medical imaging tasks remains scarce. In this
paper, we propose a novel text-driven multiplanar visual interaction framework
for semi-supervised medical image segmentation (termed Text-SemiSeg), which
consists of three main modules: Text-enhanced Multiplanar Representation (TMR),
Category-aware Semantic Alignment (CSA), and Dynamic Cognitive Augmentation
(DCA). Specifically, TMR facilitates text-visual interaction through planar
mapping, thereby enhancing the category awareness of visual features. CSA
performs cross-modal semantic alignment between the text features with
introduced learnable variables and the intermediate layer of visual features.
DCA reduces the distribution discrepancy between labeled and unlabeled data
through their interaction, thus improving the model’s robustness. Finally,
experiments on three public datasets demonstrate that our model effectively
enhances visual features with textual information and outperforms other
methods. Our code is available at https://github.com/taozh2017/Text-SemiSeg.
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