COVID-19 Pneumonia Severity Prediction using Hybrid Convolution-Attention Neural Architectures

Abstract

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 $\rho =0.92\pm 0.02$ in geographic extend and $R^2=0.72\pm 0.09,\rho =0.85\pm 0.06$ in opacity prediction.

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• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Cited by in corpus (1)

• Hierarchical Associative Memory