COVID-19 Pneumonia Severity Prediction using Hybrid Convolution-Attention Neural Architectures
This work addresses severity prediction for COVID-19 patients, representing an incremental improvement with domain-specific application.
The study tackled COVID-19 pneumonia severity prediction by proposing a hybrid convolution-attention neural framework, achieving R²=0.85±0.05 and ρ=0.92±0.02 for geographic extent and R²=0.72±0.09 and ρ=0.85±0.06 for opacity prediction.
This study proposed a novel framework for COVID-19 severity prediction, which is a combination of data-centric and model-centric approaches. First, we propose a data-centric pre-training for extremely scare data scenarios of the investigating dataset. Second, we propose two hybrid convolution-attention neural architectures that leverage the self-attention from the Transformer and the Dense Associative Memory (Modern Hopfield networks). Our proposed approach achieves significant improvement from the conventional baseline approach. The best model from our proposed approach achieves $R^2 = 0.85 \pm 0.05$ and Pearson correlation coefficient $ρ= 0.92 \pm 0.02$ in geographic extend and $R^2 = 0.72 \pm 0.09, ρ= 0.85\pm 0.06$ in opacity prediction.