Collective Intelligent Strategy for Improved Segmentation of COVID-19 from CT
This work addresses the need for fast, accurate, and low-cost diagnostic tools for COVID-19, particularly in remote or resource-limited settings, though it appears incremental as it builds on existing deep learning methods.
The authors tackled the problem of automated COVID-19 infection segmentation from CT scans by proposing the EAMC network, which achieved high sensitivity and precision in outlining infected regions and assessing severity across four public datasets.
The devastation caused by the coronavirus pandemic makes it imperative to design automated techniques for a fast and accurate detection. We propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling Attention-based Multi-scaled Convolution network (EAMC), employing Leave-One-Patient-Out (LOPO) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The Attention module combines contextual with local information, at multiple scales, for accurate segmentation. Ensemble learning integrates heterogeneity of decision through different base classifiers. The superiority of EAMC, even with severe class imbalance, is established through comparison with existing state-of-the-art learning models over four publicly-available COVID-19 datasets. The results are suggestive of the relevance of deep learning in providing assistive intelligence to medical practitioners, when they are overburdened with patients as in pandemics. Its clinical significance lies in its unprecedented scope in providing low-cost decision-making for patients lacking specialized healthcare at remote locations.