Static–Dynamic coordinated Transformer for Tumor Longitudinal Growth Prediction

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

“…We evaluated the Paper Quality by checking whether all the items related to the dataset were not blank, whether all the items related to time were not blank, and Transparency. (a) represents the evaluation of studies focusing on tumors, as cited in the following references [29,30,33,37,74,74,78]. Table (b) represents the evaluation of studies focusing on bone, as cited in the following references [43,66].…”

“…Due in part to the recent development of open software in machine learning libraries, the use of ADAM optimizer was used in 7 cases [29][30][31][32][33][34][35], Pytorch in 5 cases [29,33,[35][36][37], Numpy in 2 cases [38,39], TensorFlow in 2 cases [30,40], and OpenCV in 2 cases [41,42]. (By considering the mean square and mean of the gradient as first and second-order moments, weights can be updated on an appropriate scale for each parameter.)…”

“…This is because our study does not focus on specific machine learning techniques, making the comparison of loss functions less effective. The most commonly used loss functions in the papers reviewed in our study are L1 loss in 8 papers [31,[34][35][36][37]47,59,78], MSE in 5 papers [29,39,40,53,66], and Dice loss in 4 papers [32,33,37,74]. We also reviewed learning algorithms that do not use loss functions, such as PLS and PLSR [68,69], as well as those where the loss function is not mathematically defined but only verbally explained, as in Y-H Chang et al [42].…”