. 2021 Apr 22;34(3):630–636. doi: 10.1007/s10278-021-00449-y

Table 1.

Input, output, and loss function difference of three models

	Input	Output layer	Loss function
Model 1: not using the segmented mask	3D MRI volume	The output of a fully connected layer	Cross-entropy between the true label and predictive label (function 1, see below)
Model 2: using only the segmented mask	Only voxels within the boundary box of tumor mask	The output of a fully connected layer	Cross-entropy between the true label and predictive label (function 1)
Model 3: our novel mask-guided model	3D MR volume	The output of a fully connected layer and last conv output	1: Cross-entropy between a true label and predictive label 2: Dice coefficient between tumor mask and 3D CNN activation map (function 2)

Input

Output layer

Loss function

Model 1: not using the segmented mask

3D MRI volume

The output of a fully connected layer

Cross-entropy between the true label and predictive label

(function 1, see below)

Model 2: using only the segmented mask

Only voxels within the boundary box of tumor mask

The output of a fully connected layer

Cross-entropy between the true label and predictive label

(function 1)

Model 3: our novel mask-guided model

3D MR volume

The output of a fully connected layer and last conv output

1: Cross-entropy between a true label and predictive label

2: Dice coefficient between tumor mask and 3D CNN activation map

(function 2)

Function 1: CE (L, P) = −Σ L_i log(P_i)

DiceCoef (mask1, mask2) = 2 × |mask1 ∩ mask2|/(|mask1| +|mask2|)

Function 2: loss = CE (label, prediction) − λ × DiceCoef (class activation map, mask)

CE refers to cross-entropy, i refers to different images, L refers to label, P refers to prediction