  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.
