PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis

30 Jun 2022 · Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan ·

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.

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COVID-19 Diagnosis

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Absolute Position Encodings • Adam • BPE • Dense Connections • Depthwise Convolution • Dropout • GELU • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • PVTv2 • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer

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PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis

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