Fig. 2
From: Low-dose CT radiomics features-based neural networks predict lymphoma types
The pipeline of the study. The one with the highest SUVmax value was selected among the lymph nodes in the whole body. Two observers segmented the lesions. The inter-observer agreement of the segments was evaluated with the Dice coefficient (Mean Dice: 0.807). Inter-observer agreement was evaluated with the ICC coefficient, and features < 0.75 were eliminated. Then, LASSO regression was performed for final selection of features. Neural networks were trained and validated