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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.19364 |
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| _version_ | 1866917786175930368 |
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| author | Xie, Yuxin Zhou, Tao Zhou, Yi Chen, Geng |
| author_facet | Xie, Yuxin Zhou, Tao Zhou, Yi Chen, Geng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19364 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues Xie, Yuxin Zhou, Tao Zhou, Yi Chen, Geng Computer Vision and Pattern Recognition 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 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.19364 |