| Autor: |
Li C, Dong L, Dou Q, Lin F, Zhang K, Feng Z, Si W, Deng X, Deng Z, Heng PA |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2021 Nov; Vol. 25 (11), pp. 4140-4151. Date of Electronic Publication: 2021 Nov 05. |
| DOI: |
10.1109/JBHI.2021.3103646 |
| Abstrakt: |
The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two models teach each other during training by using their respective predicted pseudo-labels of unlabeled data. Moreover, to alleviate the adverse impacts of noisy pseudo-labels for each model, we propose a self-ensembling strategy to perform consistency regularization for the up-to-date predictions of unlabeled data, in which the predictions of unlabeled data are gradually ensembled via moving average at the end of every training epoch. We evaluate our framework on a COVID-19 dataset containing 103 CT scans. Experimental results show that our proposed method achieves better performance in the case of only 4 labeled CT scans compared to the state-of-the-art semi-supervised segmentation networks. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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