TY - GEN
T1 - SimTxtSeg
T2 - 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
AU - Xie, Yuxin
AU - Zhou, Tao
AU - Zhou, Yi
AU - Chen, Geng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Text-vision hybrid attention
KW - Textual-to-visual cue converter
KW - Weakly-supervised medical image segmentation
UR - https://www.scopus.com/pages/publications/85206897039
U2 - 10.1007/978-3-031-72111-3_60
DO - 10.1007/978-3-031-72111-3_60
M3 - 会议稿件
AN - SCOPUS:85206897039
SN - 9783031721106
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 634
EP - 644
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings
A2 - Linguraru, Marius George
A2 - Dou, Qi
A2 - Feragen, Aasa
A2 - Giannarou, Stamatia
A2 - Glocker, Ben
A2 - Lekadir, Karim
A2 - Schnabel, Julia A.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 October 2024 through 10 October 2024
ER -