bibtext: @InProceedings{ Xie_SimTxtSeg_MICCAI2024,
                   author = { Xie, Yuxin and Zhou, Tao and Zhou, Yi and Chen, Geng },
                   title = { { SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
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. Source code is available at: https://github.com/xyx1024/SimTxtSeg.
title: SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues
authors: Xie, Yuxin Zhou, Tao Zhou, Yi Chen, Geng