  Deep learning has been increasingly incorporated into various computational
pathology applications to improve its efficiency, accuracy, and robustness.
Although successful, most previous approaches for image classification have
crucial drawbacks. There exist numerous tasks in pathology, but one needs to
build a model per task, i.e., a task-specific model, thereby increasing the
number of models, training resources, and cost. Moreover, transferring
arbitrary task-specific model to another task is still a challenging problem.
Herein, we propose a task-agnostic generative and general pathology image
classifier, so called GPC, that aims at learning from diverse kinds of
pathology images and conducting numerous classification tasks in a unified
model. GPC, equipped with a convolutional neural network and a
Transformer-based language model, maps pathology images into a high-dimensional
feature space and generates pertinent class labels as texts via the
image-to-text classification mechanism. We evaluate GPC on six datasets for
four different pathology image classification tasks. Experimental results show
that GPC holds considerable potential for developing an effective and efficient
universal model for pathology image analysis.
