  The field of digital pathology has seen a proliferation of deep learning
models in recent years. Despite substantial progress, it remains rare for other
researchers and pathologists to be able to access models published in the
literature and apply them to their own images. This is due to difficulties in
both sharing and running models. To address these concerns, we introduce
WSInfer: a new, open-source software ecosystem designed to make deep learning
for pathology more streamlined and accessible. WSInfer comprises three main
elements: 1) a Python package and command line tool to efficiently apply
patch-based deep learning inference to whole slide images; 2) a QuPath
extension that provides an alternative inference engine through user-friendly
and interactive software, and 3) a model zoo, which enables pathology models
and metadata to be easily shared in a standardized form. Together, these
contributions aim to encourage wider reuse, exploration, and interrogation of
deep learning models for research purposes, by putting them into the hands of
pathologists and eliminating a need for coding experience when accessed through
QuPath. The WSInfer source code is hosted on GitHub and documentation is
available at https://wsinfer.readthedocs.io.

Tissues and Organs;Computer Vision and Pattern Recognition