SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues
<|reference_start|>SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues: 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. Source code is available at: https://github.com/xyx1024/SimTxtSeg.<|reference_end|>
@article{xie2024simtxtseg:,
    title={SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text
  Cues},
    author={Yuxin Xie, Tao Zhou, Yi Zhou, Geng Chen},
    journal={arXiv preprint arXiv:2406.19364},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19364},
    primaryClass={cs.CV}
}