@inproceedings{a2056db62485416295ad6ef47b4138ce,
title = "Towards unbiased covid-19 lesion localisation and segmentation via weakly supervised learning",
abstract = "Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images. Due to the nature of blurred boundaries, the supervised segmentation methods usually suffer from annotation biases. To support unbiased lesion localisation and to minimise the labelling costs, we propose a data-driven framework supervised by only image level labels. The framework can explicitly separate potential lesions from original images, with the help of an generative adversarial network and a lesion-specific decoder. Experiments on two COVID-19 datasets demonstrates the effectiveness of the proposed framework and its superior performance to several existing methods.",
keywords = "COVID-19, CT, GAN, Lesion localization and segmentation, Weakly supervised learning",
author = "Yang Yang and Jiancong Chen and Ruixuan Wang and Ting Ma and Lingwei Wang and Jie Chen and Zheng, \{Wei Shi\} and Tong Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 ; Conference date: 13-04-2021 Through 16-04-2021",
year = "2021",
month = apr,
day = "13",
doi = "10.1109/ISBI48211.2021.9433806",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1966--1970",
booktitle = "2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021",
address = "United States",
}