Should I accept a model with good validation loss & accuracy but bad training one? I am training a model for 3D CT scan classification (is there a nodule or not in the image).
I'm using a 3D CNN and I obtaint the following curve;

(blue curve is the validation data (10% of dataset))
as you can see, I obtain good result on both validation loss (categorical cross entropy, up to 0.16 for val) and metric (categorical accuracy) (up to 1.0 for val) but not on the training data.
I'm using a grid deformation on the training data and several dropout layer since my dataset is very small (120 samples), so it could explain the lower result on training set.
Still, I dont know if I can accept this type of result ?
The validation set having just 12 samples is too small to consider deploying a model which performs well on the validation set.
A model that performs well on the training set with 110 samples and then performs better on the small validation set would be preferable.
Since your set is too small, all though it is not prudent, you should consider running the model on the entire 120 samples to see how it performs on the entire dataset.
I presume you can't get more samples, if you can, that would be a good step.