  Standard deep learning-based classification approaches require collecting all
samples from all classes in advance and are trained offline. This paradigm may
not be practical in real-world clinical applications, where new classes are
incrementally introduced through the addition of new data. Class incremental
learning is a strategy allowing learning from such data. However, a major
challenge is catastrophic forgetting, i.e., performance degradation on previous
classes when adapting a trained model to new data. Prior methodologies to
alleviate this challenge save a portion of training data require perpetual
storage of such data that may introduce privacy issues. Here, we propose a
novel data-free class incremental learning framework that first synthesizes
data from the model trained on previous classes to generate a \ours.
Subsequently, it updates the model by combining the synthesized data with new
class data. Furthermore, we incorporate a cosine normalized Cross-entropy loss
to mitigate the adverse effects of the imbalance, a margin loss to increase
separation among previous classes and new ones, and an intra-domain contrastive
loss to generalize the model trained on the synthesized data to real data. We
compare our proposed framework with state-of-the-art methods in class
incremental learning, where we demonstrate improvement in accuracy for the
classification of 11,062 echocardiography cine series of patients.
