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

Autor:	Shan, Dandan, Li, Zihan, Chen, Wentao, Li, Qingde, Tian, Jie, Hong, Qingqi
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by ICASSP 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.00279 Zobrazit plný text záznamu View this record from Arxiv