PathML: A unified framework for whole-slide image analysis with deep learning

Abstract

The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, successful implementation of deep learning for WSI analysis is complex and requires careful consideration of model hyperparameters, slide and image artefacts, and data augmentation. Here we introduce PathML, a Python library for performing pre- and post-processing of WSIs, which has been designed to interact with the most widely used deep learning libraries, PyTorch and TensorFlow, thus allowing seamless integration into deep learning workflows. We present the current best practices in deep learning for WSI analysis, and give a step-by-step guide using the PathML framework: from annotating and pre-processing of slides, to implementing neural network architectures, to training and post-processing. PathML provides a unified framework in which deep learning methods for WSI analysis can be developed and applied, thus increasing the accessibility of an important new application of deep learning.