Pretrained or resume is nothing but a convention. When you talk about pretrained, you usually refer to weights for an specific architecture trained on an specific dataset such that results are the best possible for that dataset/architecture.
That’s why you talk about something pretrained on imagenet. It means your starting point is best weights for your architecture based on imagenet dataset.
On contrary, resume refers to the stage of training an architecture. While you are training it, it does not perform the best way possible. If you stop that training you need to resume, but you don’t consider you are training from pretrained as the state (weights you are taking) are not the best possible and it still needs to be trained.  Another think you may think about is that when you train a model it usually takes iterations/epochs to be trained. If you want to transmit to another person how many time/iterations etcetera took you to train an architecture, you need to talk about from where did you start. If you start from a more-or-less trained network, that does not transmit how good the performance was. If you say that you started from some network pretained on imagenet, people know what the performance is at the beggining and which weights you exactly used as it is an standard and it is reproducible.