  Recent years have seen great advancements in the development of deep learning
models for histopathology image analysis in digital pathology applications,
evidenced by the increasingly common deployment of these models in both
research and clinical settings. Although such models have shown unprecedented
performance in solving fundamental computational tasks in DP applications, they
suffer from catastrophic forgetting when adapted to unseen data with transfer
learning. With an increasing need for deep learning models to handle ever
changing data distributions, including evolving patient population and new
diagnosis assays, continual learning models that alleviate model forgetting
need to be introduced in DP based analysis. However, to our best knowledge,
there is no systematic study of such models for DP-specific applications. Here,
we propose CL scenarios in DP settings, where histopathology image data from
different sources/distributions arrive sequentially, the knowledge of which is
integrated into a single model without training all the data from scratch. We
then established an augmented dataset for colorectal cancer H&E classification
to simulate shifts of image appearance and evaluated CL model performance in
the proposed CL scenarios. We leveraged a breast tumor H&E dataset along with
the colorectal cancer to evaluate CL from different tumor types. In addition,
we evaluated CL methods in an online few-shot setting under the constraints of
annotation and computational resources. We revealed promising results of CL in
DP applications, potentially paving the way for application of these methods in
clinical practice.
