Visual Transformer with Statistical Test for COVID-19 Classification

• URL: http://arxiv.org/abs/2107.05334v1
• Date: Mon, 12 Jul 2021 11:48:33 GMT
• Title: Visual Transformer with Statistical Test for COVID-19 Classification
• Authors: Chih-Chung Hsu, Guan-Lin Chen, and Mei-Hsuan Wu
• Abstract summary: Most schemes for COVID-19 classification of Chest CT scan is based on a single-slice level. We propose 2-D and 3-D models to predict the COVID-19 of CT scan to tickle this issue.
• Score: 3.8361014991148785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the massive damage in the world caused by Coronavirus Disease 2019 SARS-CoV-2 (COVID-19), many related research topics have been proposed in the past two years. The Chest Computed Tomography (CT) scans are the most valuable materials to diagnose the COVID-19 symptoms. However, most schemes for COVID-19 classification of Chest CT scan is based on a single-slice level, implying that the most critical CT slice should be selected from the original CT scan volume manually. We simultaneously propose 2-D and 3-D models to predict the COVID-19 of CT scan to tickle this issue. In our 2-D model, we introduce the Deep Wilcoxon signed-rank test (DWCC) to determine the importance of each slice of a CT scan to overcome the issue mentioned previously. Furthermore, a Convolutional CT scan-Aware Transformer (CCAT) is proposed to discover the context of the slices fully. The frame-level feature is extracted from each CT slice based on any backbone network and followed by feeding the features to our within-slice-Transformer (WST) to discover the context information in the pixel dimension. The proposed Between-Slice-Transformer (BST) is used to aggregate the extracted spatial-context features of every CT slice. A simple classifier is then used to judge whether the Spatio-temporal features are COVID-19 or non-COVID-19. The extensive experiments demonstrated that the proposed CCAT and DWCC significantly outperform the state-of-the-art methods.

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