  In real-world clinical settings, traditional deep learning-based
classification methods struggle with diagnosing newly introduced disease types
because they require samples from all disease classes for offline training.
Class incremental learning offers a promising solution by adapting a deep
network trained on specific disease classes to handle new diseases. However,
catastrophic forgetting occurs, decreasing the performance of earlier classes
when adapting the model to new data. Prior proposed methodologies to overcome
this require perpetual storage of previous samples, posing potential practical
concerns regarding privacy and storage regulations in healthcare. To this end,
we propose a novel data-free class incremental learning framework that utilizes
data synthesis on learned classes instead of data storage from previous
classes. Our key contributions include acquiring synthetic data known as
Continual Class-Specific Impression (CCSI) for previously inaccessible trained
classes and presenting a methodology to effectively utilize this data for
updating networks when introducing new classes. We obtain CCSI by employing
data inversion over gradients of the trained classification model on previous
classes starting from the mean image of each class inspired by common landmarks
shared among medical images and utilizing continual normalization layers
statistics as a regularizer in this pixel-wise optimization process.
Subsequently, we update the network by combining the synthesized data with new
class data and incorporate several losses, including an intra-domain
contrastive loss to generalize the deep network trained on the synthesized data
to real data, a margin loss to increase separation among previous classes and
new ones, and a cosine-normalized cross-entropy loss to alleviate the adverse
effects of imbalanced distributions in training data.
