It was significant to us. Our base case is the xgboost model with tabular data only. We were looking at ways to augment our tabular performance, not improve imaging performance. It was a method of feature engineering for the problem.

Nothing I’m able to pass off publicly unfortunately. Just build a cnn, then concat the outputs into your original dataset :)

I've done this before for multi-modal classification tasks.
Train CNN end-to-end, take the layer before last for a dense vector of embeddings.
use that dense vector as a feature set alongside my tabular data in an XGBoost or Catboost model. Boom

Easy to do on a local machine, cumbersome to try and reliably deploy this model though.