  There exist numerous diagnostic tasks in pathology. Conventional
computational pathology formulates and tackles them as independent and
individual image classification problems, thereby resulting in computational
inefficiency and high costs. To address the challenges, we propose a generic,
unified, and universal framework, called a continuous and adaptive learning
model in pathology (CAMP), for pathology image classification. CAMP is a
generative, efficient, and adaptive classification model that can continuously
adapt to any classification task by leveraging pathology-specific prior
knowledge and learning taskspecific knowledge with minimal computational cost
and without forgetting the knowledge from the existing tasks. We evaluated CAMP
on 22 datasets, including 1,171,526 patches and 11,811 pathology slides, across
17 classification tasks. CAMP achieves state-of-theart classification
performance on a wide range of datasets and tasks at both patch- and
slide-levels and reduces up to 94% of computation time and 85% of storage
memory in comparison to the conventional classification models. Our results
demonstrate that CAMP can offer a fundamental transformation in pathology image
classification, paving the way for the fully digitized and computerized
pathology practice.
