We create neural network algorithms to learn clinical outcomes from genomics and histology. Our goal is to develop an integrated prediction frameworks that provide a holistic assessments of risk to improve clinical management.
We develop open-source tools to support the management, analysis, and integration of digital pathology images. The Digital Slide Archive, HistomicsTK, and HistomicsML empower users to apply powerful image analysis and machine-learning algorithms to their digital pathology data.
Histology has been used to diagnose disease and predict outcomes for over a century. Datasets that combine digital pathology with genomics provide a window into the molecular foundations of histologic phenomena. We use quantitative image analysis to reveal and precisely measure these relationships.
Lee Cooper, PhD
Pooya Mobadersany, MS
Sanghoon Lee, PhD
Mohamed Tageldin, MD