mcr-21-0665_supplementary_vignette_1_supp1.html (571.21 kB)

Supplementary Vignette 1 from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology

Name: Supplementary Vignette 1 from Building Tools for Machine Learning and Artificial Intelligence in Cancer Research: Best Practices and a Case Study with the PathML Toolkit for Computational Pathology
Published: 2023-12-15T14:20:05+00:00
License: https://creativecommons.org/licenses/by/4.0/
Keywords: cancer

dataset

posted on 2023-12-15, 14:20 authored by Jacob Rosenthal, Ryan Carelli, Mohamed Omar, David Brundage, Ella Halbert, Jackson Nyman, Surya N. Hari, Eliezer M. Van Allen, Luigi Marchionni, Renato Umeton, Massimo Loda

In this vignette, we use 15 publicly available images of a wide range of imaging modalities and file formats to demonstrate how PathML supports loading all of them under a simple, standardized syntax.

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ARTICLE 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.

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cancer

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CC BY

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