CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation
This work addresses the challenge of limited labeled data for COVID-19 lesion segmentation, which is incremental as it builds on existing Transformer and CNN methods.
The authors tackled the problem of automatic segmentation of COVID-19 lung lesions from CT images by proposing a novel network combining CNN and Transformer with a semi-supervised learning framework, achieving improvements of 3.0% in Dice coefficient and 8.2% in sensitivity over the base network.
The spread of the novel coronavirus disease 2019 (COVID-19) has claimed millions of lives. Automatic segmentation of lesions from CT images can assist doctors with screening, treatment, and monitoring. However, accurate segmentation of lesions from CT images can be very challenging due to data and model limitations. Recently, Transformer-based networks have attracted a lot of attention in the area of computer vision, as Transformer outperforms CNN at a bunch of tasks. In this work, we propose a novel network structure that combines CNN and Transformer for the segmentation of COVID-19 lesions. We further propose an efficient semi-supervised learning framework to address the shortage of labeled data. Extensive experiments showed that our proposed network outperforms most existing networks and the semi-supervised learning framework can outperform the base network by 3.0% and 8.2% in terms of Dice coefficient and sensitivity.