  In this work, we investigate multi-task learning as a way of pre-training
models for classification tasks in digital pathology. It is motivated by the
fact that many small and medium-size datasets have been released by the
community over the years whereas there is no large scale dataset similar to
ImageNet in the domain. We first assemble and transform many digital pathology
datasets into a pool of 22 classification tasks and almost 900k images. Then,
we propose a simple architecture and training scheme for creating a
transferable model and a robust evaluation and selection protocol in order to
evaluate our method. Depending on the target task, we show that our models used
as feature extractors either improve significantly over ImageNet pre-trained
models or provide comparable performance. Fine-tuning improves performance over
feature extraction and is able to recover the lack of specificity of ImageNet
features, as both pre-training sources yield comparable performance.
