Mohamed T. Amgad

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.