  There is no doubt that advanced artificial intelligence models and high
quality data are the keys to success in developing computational pathology
tools. Although the overall volume of pathology data keeps increasing, a lack
of quality data is a common issue when it comes to a specific task due to
several reasons including privacy and ethical issues with patient data. In this
work, we propose to exploit knowledge distillation, i.e., utilize the existing
model to learn a new, target model, to overcome such issues in computational
pathology. Specifically, we employ a student-teacher framework to learn a
target model from a pre-trained, teacher model without direct access to source
data and distill relevant knowledge via momentum contrastive learning with
multi-head attention mechanism, which provides consistent and context-aware
feature representations. This enables the target model to assimilate
informative representations of the teacher model while seamlessly adapting to
the unique nuances of the target data. The proposed method is rigorously
evaluated across different scenarios where the teacher model was trained on the
same, relevant, and irrelevant classification tasks with the target model.
Experimental results demonstrate the accuracy and robustness of our approach in
transferring knowledge to different domains and tasks, outperforming other
related methods. Moreover, the results provide a guideline on the learning
strategy for different types of tasks and scenarios in computational pathology.
Code is available at: \url{https://github.com/trinhvg/MoMA}.

Image and Video Processing;Computer Vision and Pattern Recognition