# Visual Transformer with Statistical Test for COVID-19 Classification

arXiv: https://arxiv.org/abs/2107.05334 · HTML: https://mlsanity.com/paper/2107.05334/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation](https://mlsanity.com/paper/2004.07054/) (2020)
- [Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images](https://mlsanity.com/paper/2004.06689/) (2020)
- [Targeted Self Supervision for Classification on a Small COVID-19 CT Scan Dataset](https://mlsanity.com/paper/2011.10188/) (2020)

## Built on by (most cited first, 8 of 8)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography](https://mlsanity.com/paper/2209.04242/) (2022)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images](https://mlsanity.com/paper/2111.11191/) (2021)
- [COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images](https://mlsanity.com/paper/2312.07580/) (2023)
- [Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks](https://mlsanity.com/paper/2406.17173/) (2024)
- [CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans](https://mlsanity.com/paper/2110.08721/) (2021)
- [Adaptive GLCM sampling for transformer-based COVID-19 detection on CT](https://mlsanity.com/paper/2207.01520/) (2022)

## Abstract (arXiv)

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.