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.

提出了一种新的无数据增量学习框架，通过学习类别的数据综合代替先前类别的数据存储，在引入新类别时更新网络；关键创新包括获取以往无法获取的类别的合成数据（称为 Continual Class-Specific Impression - CCSI）并有效利用这些数据来更新网络，同时采用多个损失函数来推广深度网络。