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Table 1 All network architectures are listed which are used in this manuscript.

From: Lesion probability mapping in MS patients using a regression network on MR fingerprinting

Network Loss Inputs Outputs Naming
1 MSE 35 (MRF baseline) 5 (\(T_1\), \({T_2}^*\), WM-, GM-, lesion prob. maps) MSE-5
2 MAE 35 (MRF baseline) 5 (\(T_1\), \({T_2}^*\), WM-, GM-, lesion prob. maps) MAE-5
3 LCL 35 (MRF baseline) 5 (\(T_1\), \({T_2}^*\), WM-, GM-, lesion prob. maps) LCL-5
4 MSE 35 (MRF baseline) 1 (lesion prob. map) MSE-1
5 MAE 35 (MRF baseline) 1 (lesion prob. map) MAE-1
6 LCL 35 (MRF baseline) 1 (lesion prob. map) LCL-1
7 DICE 35 (MRF baseline) 1 (lesion prob. map) DICE-1
8 MSE 2 (\(T_1\), \({T_2}^*\) map) 1 (lesion prob. map) MSE-2-1
  1. Networks 3, 5, 6, 7 were not converging into lesion probability maps
  2. The loss functions mean absolute error (MAE), mean squared error (MSE), locarithmic hyperbolic cosinus loss (LCL), and dice loss (DICE) are used. The number of outputs is either 5 (\(T_1\), \({T_2}^*\) maps and NAWM-, GM-, and lesion probability maps) or 1 (only lesion probability map)