Machine vision-assisted identification of the lung adenocarcinoma category and high-risk tumor area based on CT images

A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area

“…These steps correspond to the preprocessing stage in the original publication ( Chen et al., 2022 ), including the segmentation unit and rebalancing unit. The parameters miu and miut in step 4 correspond to the parameters μ and ρ in the original publication.…”

“…A bilateral-branch network with a knowledge distillation procedure (KDBBN) was developed for the auxiliary diagnosis of lung adenocarcinoma in the original publication ( Chen et al., 2022 ). For technical details and the general background, please refer to the original publication ( Chen et al., 2022 ).…”

“…A bilateral-branch network with a knowledge distillation procedure (KDBBN) was developed for the auxiliary diagnosis of lung adenocarcinoma in the original publication ( Chen et al., 2022 ). For technical details and the general background, please refer to the original publication ( Chen et al., 2022 ). Recent research results ( Yanagawa et al., 2017 ) have indicated the correlation between CT images and the pathological classification of lung adenocarcinoma, which provides a solid knowledge foundation in the clinical for developing the deep learning powered image analytics model.…”

“…Detailed steps include data standardization, data preprocessing, model implementation, results display through heatmaps, and statistical analysis process with Origin software or python codes. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022) .…”

“…For complete details on the use and execution of this protocol, please refer to Chen et al. (2022) .…”