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

• URL: http://arxiv.org/abs/2303.00279v1
• Date: Wed, 1 Mar 2023 07:01:29 GMT
• Title: Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment
• Authors: Dandan Shan, Zihan Li, Wentao Chen, Qingde Li, Jie Tian, Qingqi Hong
• Abstract summary: We propose C2FVL, a Coarse-to-Fine segmentation framework via Vision-Language alignment to merge text information. The introduction of 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.
• Score: 6.754040546065469
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. The introduction of 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.

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