Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment

Authors

Dandan Shan^†, Zihan Li^†, Wentao Chen, Qingde Li, Jie Tian, and Qingqi Hong
† co-first author

Abstract

Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19. However, there are few relevant studies due to the lack of detailed information and high-quality annotation in the COVID-19 dataset. To solve the above problem, we propose C2FVL, a Coarse-to-Fine segmentation framework via Vision-Language alignment to merge text information containing the number of lesions and specific locations of image information. Introducing text information allows the network to achieve better prediction results on challenging datasets. We conduct extensive experiments on two COVID-19 datasets, including chest X-ray and CT, and the results demonstrate that our proposed method outperforms other state-of-the-art segmentation methods.

Article

BibTeX

@article{dandanshan2023,
  title={Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment},
  author={Dandan Shan and Zihan Li and Wentao Chen and Qingde Li and Jie Tian and Qingqi Hong},
  journal={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2023},
  month={}
}