PathAI develops machine learning models which provide insights for drug development R&D, for powering clinical trials, and for making diagnoses. To this end, PathAI leverages PyTorch for slide level inference using a variety of methods including graph neural networks (GNN) as well as multiple instance learning. In this context, “slides” refers to full size scanned images of glass slides, which are pieces of glass with a thin slice of tissue between them, stained to show various cell formations. PyTorch enables our teams using these different methodologies to share a common framework which is robust enough to work in all the conditions we need. PyTorch’s high level, imperative, and pythonic syntax allows us to prototype models quickly and then take those models to scale once we have the results we want. 
Multi-instance learning on gigabyte images