Justifying Diagnosis Decisions by Deep Neural Networks
Table 3: Classification accuracy for different classification models.
['Model', 'Accuracy', '[ITALIC] σ']
Additionally, we compare the classification accuracy on the test set of our multi-task inverse mapping I, to several recent state-of-the-art classification methods that are trained using only the classification loss. Note that the Vgg19 model training failed to obtain good results in several runs, leading to an overall lower accuracy and higher standard deviation.