Extreme Imbalance in Binary Classification

Scenario

You are training a deep neural network for a binary classification task where the positive class is very rare. You need a training setup that can still learn useful signal from the minority class without collapsing to majority-class predictions.

Question

How would you handle extreme class imbalance when training a deep neural network for binary classification, both at the data level and the loss-function level?