PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis
This work addresses automated COVID-19 diagnosis for medical applications, but it appears incremental as it builds on existing Transformer and PVT methods.
The authors tackled COVID-19 diagnosis from lung CT scans by proposing PVT-COV19D, a framework using Transformer models and a modified PVTv2, and demonstrated its effectiveness on the COV19-CT-DB dataset.
With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis framework based on lung CT scan images, the PVT-COV19D. In order to accommodate the different dimensions of the image input, we first classified the images using Transformer models, then sampled the images in the dataset according to normal distribution, and fed the sampling results into the modified PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method.