PathML Presentation on June 6
Join us on Monday, June 6 at 10-11 AM ET for a presentation titled PathML: An open-source software toolkit for computational pathology
Summary:
Imaging datasets in cancer research have grown exponentially in size and information density in recent years, driven chiefly by two trends:
Increasing adoption of digital pathology workflows at departmental and institutional scale (large n datasets)
Emerging technologies in highly multiplexed imaging and spatial omics (high dimensional datasets)
The unprecedented scale of today’s 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 approaches from machine learning and computer vision. PathML is a software toolkit designed to lower the barrier to entry for computational pathology, enabling researchers to develop streamlined, scalable, fully customized end-to-end image analysis pipelines, with a unified framework for brightfield, multiplexed immunofluorescence, and spatial omics images and support for 160+ file formats. Developed at Dana-Farber Cancer Institute and Weill Cornell Medicine, PathML is currently being used by 7+ research groups and 2 imaging core facilities across the two institutions. PathML is an open-source project freely available on GitHub, with complete documentation, tutorials, and example vignettes and more than 15,000 downloads worldwide. We welcome anyone interested in collaborating or learning more to contact us at PathML@dfci.harvard.edu or visit www.pathml.org for more information.
Speakers:
Jacob Rosenthal is a data scientist in the Artificial Intelligence Operations and Data Science Services group at Dana-Farber Cancer Institute, where he leads development of data infrastructure and analytics to enable the Institute’s efforts in digital pathology research and operations. He is also an affiliate data scientist in the Department of Pathology and Laboratory Medicine and Weill Cornell Medicine. He received his M.Sc. in Health Data Science from the Harvard T.H. Chan School of Public Health.
Renato Umeton serves as Associate Director of Artificial Intelligence Operations and Data Science Services in the Department of Informatics & Analytics at Dana-Farber Cancer Institute. He leads design, implementation, and deployment of AI and data science solutions for the Institute, as well as customized AI and data science support in laboratories, centers, and departments, spanning data modalities including digital pathology, natural language, radiology, and MLOps. He received his PhD in Mathematics and Informatics and holds affiliations at Weill Cornell Medicine, Harvard T.H. Chan School of Public Health, and MIT.