I found this Q. a year or so after it was asked, and I still don't think there are any studies that have comprehensively tested this.
When most people discuss transfer learning, what they think of is:
"Will imagenet weights help me get better accuracy (or at least
speed up training) when using [insert popular google/facbook
AI/microsoft architecture] on my data."
In reality, if I have medical images of arm bones, and I've previously trained a model on leg bones, that would probably be more applicable transfer learning than using imagenet weights... so stating that "de novo is better" or "transfer learning is better" is kind of meaningless, because you may just not have access to more relevant weights for transfer learning than imagenet.
The early layers always contain 'simple' features, and these are probably always somewhat transferable to any other visual problem; providing the scale of the objects of interest in the images are not drastically different from the original data. But to answer the question directly: no, there is no “proven” disadvantage to transferring weights from a previously trained network.