Mohamed Amgad

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models

Congzheng Song, Safoora Yousefi, Fatemeh Amrollahi, 2017 .

Learning Clinical Outcomes from Heterogeneous Genomic Data Sources

Amirreza Shaban, Mohamed Amgad, Safoora Yousefi, 2019, 1904.01637.

Interactive phenotyping of large-scale histology imaging data with HistomicsML

Daniel J. Brat, Lee Cooper, David A. Gutman, 2017 .

An Ensemble-based Active Learning for Breast Cancer Classification

David Gutman, Mohamed Amgad, Lee Cooper, 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Predicting cancer outcomes from histology and genomics using convolutional networks

Jill S Barnholtz-Sloan, Safoora Yousefi, Mohamed Amgad, 2017 .

Digital and Computational Pathology: Bring the Future into Focus

Vijayan K. Asari, Vijayan K. Asari, Nikolaos Grammalidis, 2019, Journal of Pathology Informatics.

Interactive phenotyping of large-scale histology imaging data with HistomicsML

Sanghoon Lee, Mohamed Amgad, David A Gutman, 2017, bioRxiv.

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer pdf

Ny, Chicago, Tx, 2021, GigaScience.

A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning pdf

Mohamed Amgad, Lantian Zhang, Lee A. D. Cooper, 2021, ArXiv.

Extending Ripley’s K-Function to Quantify Aggregation in 2-D Grayscale Images

Mohamed Amgad, M. Amgad, Anri Itoh, 2015, PloS one.