NSF MRI: High Performance Digital Pathology Using Big Data and Machine Learning

What's New

(20220113) We are pleased to announce v2.0.0 of the TUH DPATH Corpus. This release contains a breast tissue subset and is described here. There are 3,505 annotated images partitioned into training, development and blind evaluation sets.

(20211215) We have completed scanning the Fox Chase Cancer Center subset and now have scanned over 80,000 slides.

(20200115) We reached an agreement with Fox Chase Cancer Center to scan all the sldies in their Biosample Repository Facility.

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Project Summary

In this NSF-funded project, we are developing a digital imaging system using big data and machine learning algorithms to automatically characterize pathology slides. We have developed a sustainable facility to rapidly collect automatically annotated whole slide images. This project is producing the necessary data resources to support the development of high performance deep learning models.

Over 10M slides are read each year in the U.S. alone. Tapping into a fraction of this data will allow significant advancement of the science. Healthcare providers and machine learning researchers will be able to access an open source high-quality searchable archive of clincial data. More information on this project can be found here.


A Cost-Effective Image Management Platform

This NSF Major Research Instrumentation (MRI) grant supported the purchase of a Leica Aperio AT2 scanner as the platform used to convert pathology slides to digital images. This scanner can scan 50 high quality TIFF images with lossless compression per hour.

We have also developed a very cost-effective Petabyte file store based on off-the-shelf components to store our database of 1M pathology images. To learn more about our clustered computing environment being developed to support this research program, read this overview.