Figure 3

The training and validation loss for the U-Net model with and without deep supervision. The x-axis indicates the number of epochs, which is the number of times the deep learning model has passed through the entire training data during the training phase. The y-axis represents the loss value which implies how well the model behaves after each epoch; the lower the loss, the better a model. The dashed lines show the validation losses while the solid lines show the training losses. For the model with the deep supervision (blue lines), the training loss converges at a considerably faster rate, and the converged loss value is lower than the converged value of the model without deep supervision (green lines).