Evaluation methods

As the main goal of transfer learning is to recover additional small details that are lost in traditional deep learning methods, checking small textures in the augmented images is of great significance. In this study, we used the same window level for all the augmented images and ground truth images, which was normalized from 0 to 1. Difference maps were also generated between augmented images and ground truth images to determine which part displayed the most difference. Additionally, to evaluate the performance of transfer learning, we extracted the lung area from the body area using a novel method from MathWorks (24) to view any improvements in different parts of the body.

In addition to the visual inspection, we also used the peak signal-to-noise ratio (PSNR), which is defined as follows:

where T and G denote the reconstructed images and ground-truth images, respectively, M and N are the number of pixels for a row and a column, respectively, and the structure similarity index matrix (SSIM), which is defined as follows:

where μT is an average of T, σG2 is a variance of G, σTG is a covariance of T and G (two variables stabilize the division; that is c1=(k1L)2 and c2=(k2L)2), L is a dynamic range of the pixel intensities, k₁ and k₂ are constants by default (k₁ =0.01 and k₂ =0.03).