(20200115) We continue to scan slides for the TUH Tumor Board where revered pathologists discuss cancer cases with our high definition digital slides.
(20191120) Under the mentorship of Dr. Jhala, Director of Anatomic Pathology and Cytology at TUH, we are creating a pilot corpus of breast cancer. We annotate four features: ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), benign tissue, and inflammation.
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.