## 4 Experimental Design  
### Datasets  
We demonstrate ASEM-CAD using three publicly available computed tomography (CT) datasets: the National Lung Screening Trial (NLST), the Lung Image Database Consortium (LIDC-IDRI), and Kaggle Data Science Bowl 2017 (Kaggle2017). NLST was a randomized trial conducted at multiple centers to screen lung cancer with low-dose CT for individuals aged 55-74 years with a significant smoking history [78]. Electronic calipers were used by radiologists specialized in standardized image interpretation to measure nodule size. Of the 26,722 patients in the CT screening arm of the NLST, 16,684 were excluded, as no abnormality was recorded in the NLST database. In our study, CT studies from 4075 scans from the NLST dataset were used. Each CT scans was annotated with 1 if the patient was diagnosed with cancer, and the non-cancerous CT scans were labeled as 0. Of the 4075 scans, 639 patients (15.7%) were diagnosed with lung cancer. Kaggle Data Science Bowl provided labeled low-dose CT scans from 1375 patients to facilitate the development of novel machine learning algorithms for automated CT diagnosis [79]. This includes images with associated binary labels for 356 patients (25.8%) diagnosed with lung cancer within one year of the scan. The LIDC-IDRI dataset [80] is a publicly available and web-accessible international resource initiated by the National Cancer Institute (NCI), further developed by the Foundation for the National Institutes of Health (FNIH), and supported by the U.S. Food and Drug Administration (FDA). The LIDC dataset consists of thoracic computed tomography (CT) scans for diagnostic and lung cancer screening purposes. The scans contain marked-up annotated lesions provided by four experienced thoracic radiologists. This dataset is the result of a collaboration between seven academic centers and eight medical imaging companies, consisting of 1018 CT cases with thoracic CT scan images in the DICOM format, along with an additional XML file per patient. Based on the nodule size, the lesions were categorized into three categories: nodules \(>\) or =3 mm, nodules \(<\)3 mm, and non-nodules \(>\) or =3 mm. Furthermore, it consists of malignancy ratings from 1 to 5 based on the nodule size, type, and characteristics. In the LIDC-IDRI dataset, nodules that are equal to or larger than 3 mm were annotated by four board-certified radiologists to include subtlety, internal structure, calcification, sphericity, margin, lobulation, spiculation, and texture image characteristics. Subtlety refers to the degree of contrast between the lung and its surrounding tissues [80]. Internal structure relates to the components that are present within the nodule. Calification is the appearance of calcium in the nodule, with smaller nodules more likely to exhibit visible calcium. A central, non-central, laminated, or popcorn calcification rating is highly indicative of a benign nodule. Sphericity refers to the roundness of the nodule. Margin relates to how well-defined the edges of the nodule appear. Lobulation is an assessment of whether the nodule has a visible lobular shape, which is a sign that the nodule is benign. Spiculation refers to the presence of spicules or spike-like formations along the border of the nodule, with a spiculated margin indicating malignancy. Finally, texture refers to the internal density of the nodule and is an important characteristic when differentiating between partly solid and non-solid textures, which can complicate the process of establishing the nodule boundary. A summary of the three datasets is shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Samples** & **Positive Samples** & **Type** \\ \hline NLST & 4075 & 639 (15.7\%) & Whole CT scans \\ Kaggle17 & 1375 & 356 (25.8\%) & Whole CT scans \\ LIDC-IDRI & 4253 & 1653 (38.9\%) & 3D Nodules from \\  & & & 1018 CT scans \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of datasets.