# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 2 Related Works
It processes single-spot images, integrating aCNN for local features, a Transformer for global learning, and a GNN for cell organization analysis, which enhances interpretability. The model was evaluated on HER2+ (32 sections, eight patients), cSCC (12 sections, four patients), and spatialLIBD (59,904 spots, 20 sections, 3 donors). TCGN achieves the highest median PCC (0.232), outperforming ST-Net, HisToGene, and Hist2 ST with fewer parameters (86.24 M) and lower GPU memory. [PERSON] et al. [19] proposed ICEv2, an interpretable method that assigns class-relevance probabilities to individual patches using adversarial normalization, circumventing the limitations of traditional attention-based visualizations. ICEv2 identifies foreground (task-relevant) and background patches without modifying the model's architecture or retraining weights. The method produces fine-grained visual saliency by interpreting output logits at the patch level rather than relying on [CLS] token contributions. Applied to segmentation datasets, ICEv2 demonstrated strong performance in visual localization, offering a viable pathway for producing trustworthy visual explanations in healthcare settings. [PERSON] et al. [20] addressed ViT inefficiencies by introducing Token Reduction via an Attention-Based Multilayer Network (TRAM). TRAM constructs a graph from multilayer attention matrices and computes centrality scores to prune less informative tokens at each transformer layer. Unlike methods that rely on [CLS] tokens or retraining, TRAM performs static, architecture-agnostic pruning, which maintains classification performance while substantially reducing memory usage and inference time. These characteristics are highly desirable in real-time or embedded diagnostic systems, where latency and resource consumption are constrained. [PERSON] et al. [21] developed Transformer Input Sampling (TIS), a saliency technique that avoids the artifact generation associated with conventional perturbation-based methods, such as RISE or Score-CAM. TIS leverages the inherent flexibility of ViTs to handle variable-length token sequences, enabling interpretability by selectively omitting tokens post-embedding and measuring their impact on class predictions. Sampling embedded token subsets and assessing prediction shifts, TIS constructs faithful, class-specific saliency maps without corrupting the input image. This strategy ensures the generation of clinically meaningful explanations, which is essential for radiological review in COVID-19 diagnosis. Artificial intelligence (AI) and deep learning (DL) have transformed CT-based diagnostics, allowing automated analysis and interpretation of intricate visual data. Convolutional Neural Networks (CNNs), widely used in medical imaging, have effectively captured long-range dependencies and complex spatial relationships across an image in detecting localized lung abnormalities associated with COVID-19 [22]. However, CNNs have limits, and Vision Transformers (ViTs), which use self-attention mechanisms to identify global patterns, offer an alternative by modeling image-wide dependencies. This global perspective enables ViTs to detect broader image patterns but can sometimes overlook local structural details essential for clinical precision [23]. To address these limitations, hybrid models that combine CNNs, ViTs, and Graph Neural Networks (GNNs) have gained attention. GNNs focus on relational data within images, representing patches as nodes and spatial relationships as edges, which is crucial for capturing local details. [PERSON] et al. showed that incorporating spatial information through a GNN could improve diagnostic accuracy, supporting the potential of hybrid models for the detection of COVID-19 in CT imaging [24]. As summarized in Table 1, a wide range of deep learning approaches have been proposed for COVID-19 detection using chest CT and X-ray images. These methods vary significantly in terms of datasets used, network architectures, and reported performance.