1. SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues
- Author
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Xie, Yuxin, Zhou, Tao, Zhou, Yi, and Chen, Geng
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Weakly-supervised medical image segmentation is a challenging task that aims to reduce the annotation cost while keep the segmentation performance. In this paper, we present a novel framework, SimTxtSeg, that leverages simple text cues to generate high-quality pseudo-labels and study the cross-modal fusion in training segmentation models, simultaneously. Our contribution consists of two key components: an effective Textual-to-Visual Cue Converter that produces visual prompts from text prompts on medical images, and a text-guided segmentation model with Text-Vision Hybrid Attention that fuses text and image features. We evaluate our framework on two medical image segmentation tasks: colonic polyp segmentation and MRI brain tumor segmentation, and achieve consistent state-of-the-art performance., Comment: accepted by MICCAI 2024
- Published
- 2024