I went to a medical imaging workshop recently, and the consensus was that deep learning approaches will completely replace classical compressed sensing. They are using the same principles of acquiring randomized samples, so it's still compressed sensing, they just produce dramatically better results than classical CS techniques.

> They are using the same principles of acquiring randomized samples, so it's still compressed sensing,

See the second part of my comment. This is only in principle. In practice compressed sensing uses a higher frequency basis and more importantly, this basis is generally not learned, preventing common case bias. Ie a rare condition won't be ignored because it isn't statistically common enough for the NN model to learn.