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.
翻译:由于Corona病毒疾病 2019 SARS-COV-2(COVID-19)给世界造成了巨大损害,在过去两年中提出了许多相关的研究课题。胸前光谱成像扫描(CT)是诊断COVID-19症状的最有价值的材料;然而,对胸前光谱成像扫描(COVID-19-19)的多数分类办法是基于单虱水平,这意味着应从最初的CT扫描量中选择最关键的CT切片。我们同时提议采用2-D和3-D型模型来预测CT扫描的COVI-19-19(COVI-19),以弥补这一问题。在我们的2-D模型中,我们采用深Wilcoxon签名-感像仪扫描(DWCC)是用来确定每次CT扫描切片的重要性,以克服前面提到的问题。此外,为了充分发现切片的背景,我们根据任何主干网并随后将C-C-C-19扫描的COCT19(C-C-C-C-C-C-C-Stradeal-S-Tradeal-S-Trading Atracelex)的特性输入了我们内部的S-Strade-Trade-Tracele-Trade-S-Tracelex-Trade-S-Trade-S-S-S-S-Tracelex-S-S-S-S-S-lex-de-de-de-lex-S-de-de-de-de-de-de-leftlex-leftleftlex-de-de-leftal-leftleft-left-left-de-de-lemental-Ileft-de-de-de-de-de-de-lex,这是每一个-de-de-S-S-S-S-S-lex 的每一个-de-de-S-de-de-de-de-de-de-de-de-S-leftal-de-de-de-de-de-de-de-de-S-S-S-de-S-S-S-de-de-de-de-de-de-de-de-S-S-S-S-S-de-de-de-S-S-S-de-de-