## 4 Experiments  
### Dataset and Experimental Settings  
Public datasets often contain a relatively small amount of data with limited distribution, and algorithms that perform well on them also require validation on private datasets to ensure robustness and transferability. Clinical datasets better represent the real-world scenarios encountered in healthcare. Clinical datasets may include challenges like artifacts, noise, and protocol variations that are not fully represented in curated open datasets. Testing on this real-world clinical data allows us to assess the robustness of algorithms to factors that can significantly impact segmentation accuracy and reliability. Open datasets alone may not accurately reflect the challenges of clinical practice, leading to overestimated or unrealistic performance metrics. We assess our framework performance on two datasets, the publicly available COVID-SemiSeg Segmentation Dataset (2018) and a clinical COVID-HSS image dataset. COVID--The SemiSeg dataset comprises 100 axial 2D CT images from various patients. These CT images were gathered by the Italian Society of Medicine and Interventional Radiology. Radiologists have employed diverse labels for segmenting the CT images to delineate areas of lung infection. It has a limited sample size, consisting of only 100 annotated images. For all the experiments, we employ basic data augmentations such as random rotation and flipping. Our transYL introduces Swin Transformer modules into Backbone layers 7 and 9, reducing computation via convolutions. The input resolution is configured as 8 \(\times\) 8 with patch size P 16, respectively, unless specified otherwise. Consequently, four 2\(\times\) up sampling blocks are cascaded in CUP consecutively to achieve the full resolution. The default batch size is 6 and the default training iterations is 20 k for COVID-SemiSeg. In the model training, selecting different optimizers may greatly affect network performance. Stochastic Gradient Descent (SGD) is used to update the parameters of our network based on the gradient information of backpropagation in order to reduce the calculated value of the loss function. SGD was chosen as the optimizer due to its computational efficiency with mini-batches, adaptability to noisy gradients, helping with the escape of sharp minima suited for deep neural networks, regularization benefits improving generalization, and faster convergence with momentum acceleration, striking a balance between efficiency, prevention of overfitting, and flexible tuning of hyper-parameters. All experiments are conducted on Nvidia RTX2080s Ti. Baselines: For the infection region experiments, we benchmark the proposed transYLmtMT against classical segmentation models in the medical domain, namely, U-Net (2018), Att-UNet (2018), Inf-Net (2018) and Trans-Inf-Net (2018). Thorough validation is essential for developing trust in automatic segmentation models prior to clinical use. Comprehensive validation provides confidence that these models can generalize to new data while maintaining reliable performances across diverse clinical scenarios. Key validation techniques include quantitative metrics to evaluate segmentation accuracy, qualitative visual analysis for assessing clinical utility, comparisons against baseline methods, and incorporation of feedback from clinicians. Ultimately, rigorous multifaceted validation combining metrics, visual analysis, comparisons, and clinician input enables a holistic understanding of model capabilities, limitations, and clinical relevance. Evaluation Metrics: Following [51; 52], we use three widely adopted metrics, i.e., the Dice similarity coefficient, Specificity (Spec. ), Sensitivity (Sen.), and Precision (Prec.) and three detection metrics, i.e., Enhance--alignment Measure(\(E_{\xi}\)) [52], Mean Absolute Error (MAE), and Structure Measure (\(S_{a}\)) [51]. In our assessment, we select S3 as the final prediction (\(S_{p}\)) with a sigmoid function.