Lee Cooper

This study evaluates how well frozen pathology foundation models transfer to cancer-related H&E and neurodegenerative IHC tasks, finding better performance on H&E tasks than on specialized IHC tasks.

This study identifies interstitial macrophages as key drivers of chronic lung allograft dysfunction and suggests CSF1R as a therapeutic target.

The paper develops an AI framework to analyze blood scans and predict patient outcomes by characterizing tumor-immune ecosystems in circulation.

TASI quantifies tumor spheroid dynamics through spatiotemporal segmentation, feature extraction, modeling, and statistical analysis, demonstrating its utility in analyzing non-small cell lung cancer spheroids.

This study uses deep learning to predict cancer patient outcomes by integrating histology images and genomic data, showing improved performance over current clinical methods for glioma survival prediction.

HistomicsML enables interactive machine learning for analyzing histology images to identify phenotypes and predict survival in gliomas.

This paper applies deep learning and Bayesian optimization to predict cancer clinical outcomes from genomic data, showing improved prognostic accuracy through transfer learning and providing an open-source framework.