For the pedantic among you assume the AUC above is determined via k-fold cross-validation with a very large number of folds and that we don’t mix samples from the same patient between folds
As a general rule of thumb, I agree with you, explainability techniques might often help with generalization. Or at least be intermixed with them. For example, to use techniques that alter the input space, it helps to train with dropout and to have certain activations that could be seen as promoting homogenous behavior on OOD data
Regrading the thought experiment:
As a general rule of thumb, I agree with you, explainability techniques might often help with generalization. Or at least be intermixed with them. For example, to use techniques that alter the input space, it helps to train with dropout and to have certain activations that could be seen as promoting homogenous behavior on OOD data