  Purpose: Deep Neuroevolution (DNE) holds the promise of providing radiology
artificial intelligence (AI) that performs well with small neural networks and
small training sets. We seek to realize this potential via a proof-of-principle
application to MRI brain sequence classification.
  Methods: We analyzed a training set of 20 patients, each with four
sequences/weightings: T1, T1 post-contrast, T2, and T2-FLAIR. We trained the
parameters of a relatively small convolutional neural network (CNN) as follows:
First, we randomly mutated the CNN weights. We then measured the CNN training
set accuracy, using the latter as the fitness evaluation metric. The fittest
child CNNs were identified. We incorporated their mutations into the parent
CNN. This selectively mutated parent became the next generation's parent CNN.
We repeated this process for approximately 50,000 generations.
  Results: DNE achieved monotonic convergence to 100% training set accuracy.
DNE also converged monotonically to 100% testing set accuracy.
  Conclusions: DNE can achieve perfect accuracy with small training sets and
small CNNs. Particularly when combined with Deep Reinforcement Learning, DNE
may provide a path forward in the quest to make radiology AI more human-like in
its ability to learn. DNE may very well turn out to be a key component of the
much-anticipated meta-learning regime of radiology AI algorithms that can adapt
to new tasks and new image types, similar to human radiologists.
