Automatic Interpretation of Digital Pathology Images Using Deep Learning
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 slide images. This project has produced the necessary data resources to support the development of high performance deep learning models.
We had proposed a collaborative and interdisciplinary project to
detect and characterize cancerous cells in digitized images of
pathology slides, while also producing the world's largest catalog
of research-grade digital pathology slides.
The research allowed our uniquely qualified team, which spans the disciplines of pathology, engineering and computer science, to pursue center-level investments from NSF and NIH.
Over 10 million slides are read each year in the U.S. alone. Tapping into a fraction of this data allows significant advancement of the science. Healthcare providers and machine learning researchers will be able to access an open source high-quality searchable archive of clinical data. More information on this project can be found here.
Our initial goal will be to combine image data extracted from a digital slide scanner with text data extracted from medical reports. Pathology reports contain unstructured text data that describe patient histories, medications and diagnoses.
We are developing our database, processing high-resolution digital pathology images, and performing integrated text processing using Visio-and-Language queries.