|Title||Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology.|
|Publication Type||Journal Article|
|Year of Publication||2022|
|Authors||Rosenthal J, Carelli R, Omar M, Brundage D, Halbert E, Nyman J, Hari SN, Van Allen EM, Marchionni L, Umeton R, Loda M|
|Journal||Mol Cancer Res|
|Date Published||2022 02|
|Keywords||Artificial Intelligence, Humans, Machine Learning, Neoplasms, Research Design|
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.
|Alternate Journal||Mol Cancer Res|
|Grant List||F31 CA250136 / CA / NCI NIH HHS / United States |
P50 CA090381 / CA / NCI NIH HHS / United States
U01 CA231776 / CA / NCI NIH HHS / United States
P50 CA211024 / CA / NCI NIH HHS / United States
Luigi Marchionni, M.D., Ph.D. Massimo Loda, M.D.