H. Elliott

发表

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer

Andrew H. Beck, Aditya Khosla, Amaro Taylor-Weiner, 2020, bioRxiv.

Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

Dayong Wang, B. Glass, M. Montalto, 2022, Modern Pathology.

Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

A. Khosla, Dayong Wang, B. Glass, 2022, Modern Pathology.

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Andrew H. Beck, James A. Diao, Wan Fung Chui, 2021, Nature Communications.

Machine learning-based identification of predictive features of the tumor micro-environment and vasculature in NSCLC patients using the IMpower150 study.

A. Khosla, J. Fridlyand, A. Beck, 2020 .

A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH

A. Khosla, V. Wong, O. Carrasco-Zevallos, 2021, Hepatology.

AI-based histologic scoring enables automated and reproducible assessment of enrollment criteria and endpoints in NASH clinical trials

I. Wapinski, A. Taylor-Weiner, H. Elliott, 2023, medRxiv.

Abstract PD6-04: Deep-learning based prediction of homologous recombination deficiency (hrd) status from histological features in breast cancer; a research study

James A. Diao, Wan Fung Chui, A. Khosla, 2021 .