  Purpose: The aim of this work is to develop a neural network training
framework for continual training of small amounts of medical imaging data and
create heuristics to assess training in the absence of a hold-out validation or
test set.
  Materials and Methods: We formulated a retrospective sequential learning
approach that would train and consistently update a model on mini-batches of
medical images over time. We address problems that impede sequential learning
such as overfitting, catastrophic forgetting, and concept drift through PyTorch
convolutional neural networks (CNN) and publicly available Medical MNIST and
NIH Chest X-Ray imaging datasets. We begin by comparing two methods for a
sequentially trained CNN with and without base pre-training. We then transition
to two methods of unique training and validation data recruitment to estimate
full information extraction without overfitting. Lastly, we consider an example
of real-life data that shows how our approach would see mainstream research
implementation.
  Results: For the first experiment, both approaches successfully reach a ~95%
accuracy threshold, although the short pre-training step enables sequential
accuracy to plateau in fewer steps. The second experiment comparing two methods
showed better performance with the second method which crosses the ~90%
accuracy threshold much sooner. The final experiment showed a slight advantage
with a pre-training step that allows the CNN to cross ~60% threshold much
sooner than without pre-training.
  Conclusion: We have displayed sequential learning as a serviceable
multi-classification technique statistically comparable to traditional CNNs
that can acquire data in small increments feasible for clinically realistic
scenarios.
