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

What's New

(20240520) We have migrated our DPATH resources to a central location here where you will find all our data releases. Over the next few weeks we will be releasing several very large DPATH datasets.

(20211215) We have completed scanning the Fox Chase Cancer Center subset and now have scanned over 120,000 slides.

(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.





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 databases. To learn more about our clustered computing environment being developed to support this research program, read this overview.