Implementing Deep Learning Algorithms in Anatomic Pathology Using Open-source Deep Learning Libraries

The application of artificial intelligence technologies to anatomic pathology has the potential to transform the practice of pathology, but, despite this, many pathologists are unfamiliar with how these models are created, trained, and evaluated. In addition, many pathologists may feel that they do not possess the necessary skills to allow them to embark on research into this field. This article aims to act as an introductory tutorial to illustrate how to create, train, and evaluate simple artificial learning models (neural networks) on histopathology data sets in the programming languagePythonusing the popular freely available, open-source librariesKeras,TensorFlow,PyTorch, andDetecto. Furthermore, it aims to introduce pathologists to commonly used terms and concepts used in artificial intelligence.