Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology
Abstract
Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.com.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 08, 2021
- Source ID
- 10.1158/1541-7786.mcr-21-0665
Entities
People
- David Brundage
- Eliezer M. Van Allen
- Ella Halbert
- Jackson Nyman
- Jacob Rosenthal
- Luigi Marchionni
- Massimo Loda
- Mohamed Omar
- Renato Umeton
- Ryan Carelli
- Surya N. Hari
Organizations
- National Cancer Institute
- National Institutes of Health
- United States Department of Defense